case study brain trauma

CASE REPORT article

Case report: an mri traumatic brain injury longitudinal case study at 7 tesla: pre- and post-injury structural network and volumetric reorganization and recovery.

• 1 Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
• 2 Department of Rehabilitation and Human Performance, Brain Injury Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, United States
• 3 Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
• 4 Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
• 5 Department of Computer Science, Mathematics, Physics, and Statistics University of British Columbia, Kelowna, BC, Canada

Importance: A significant limitation of many neuroimaging studies examining mild traumatic brain injury (mTBI) is the unavailability of pre-injury data.

Objective: We therefore aimed to utilize pre-injury ultra-high field brain MRI and compare a collection of neuroimaging metrics pre- and post-injury to determine mTBI related changes and evaluate the enhanced sensitivity of high-resolution MRI.

Design: In the present case study, we leveraged multi-modal 7 Tesla MRI data acquired at two timepoints prior to mTBI (23 and 12 months prior to injury), and at two timepoints post-injury (2 weeks and 8 months after injury) to examine how a right parietal bone impact affects gross brain structure, subcortical volumetrics, microstructural order, and connectivity.

Setting: This research was carried out as a case investigation at a single primary care site.

Participants: The case participant was a 38-year-old female selected for inclusion based on a mTBI where a right parietal impact was sustained.

Main outcomes: The main outcome measurements of this investigation were high spatial resolution structural brain metrics including volumetric assessment and connection density of the white matter connectome.

Results: At the first scan timepoint post-injury, the cortical gray matter and cerebral white matter in both hemispheres appeared to be volumetrically reduced compared to the pre-injury and subsequent post-injury scans. Connectomes produced from whole-brain diffusion-weighted probabilistic tractography showed a widespread decrease in connectivity after trauma when comparing mean post-injury and mean pre-injury connection densities. Findings of reduced fractional anisotropy in the cerebral white matter of both hemispheres at post-injury time point 1 supports reduced connection density at a microstructural level. Trauma-related alterations to whole-brain connection density were markedly reduced at the final scan timepoint, consistent with symptom resolution.

Conclusions and Relevance: This case study investigates the structural effects of traumatic brain injury for the first time using pre-injury and post-injury 7 Tesla MRI longitudinal data. We report findings of initial volumetric changes, decreased structural connectivity and reduced microstructural order that appear to return to baseline 8 months post-injury, demonstrating in-depth metrics of physiological recovery. Default mode, salience, occipital, and executive function network alterations reflect patient-reported hypersomnolence, reduced cognitive processing speed and dizziness.

Introduction

Traumatic brain injury (TBI) is a leading cause of disability worldwide, particularly in young and military populations, with well-documented links to psychiatric and neurodegenerative pathology ( 1 ). Patients who experience mild traumatic brain injury (mTBI) typically report vestibular, sensory, cognitive or emotional symptoms that persist for several months after injury ( 2 ). Patients experiencing mTBI typically show low frequency of positive MRI findings at 6 months post-injury, highlighting the need for more sophisticated and sensitive imaging techniques in the clinical investigation of mTBI ( 3 , 4 ). Previous studies have reported the utility of diffusion-weighted imaging and tractography methodology in determining the presence of neuronal injury in cases where conventional neuroimaging findings are negative ( 5 , 6 ), as they allow examination of fiber- and tract-related pathology. Tracking of the spinothalamic tract in a mTBI case demonstrated thinning and discontinuation of fibers at the subcortical white matter in mTBI patients with no conventional radiological abnormalities ( 6 ), and the corticobulbar tract and fornix exhibited similar narrowing and discontinuations in an mTBI case study caused by violence ( 5 ). It is probable that white matter damage, common in TBI due to both indirect shearing forces and direct damage, may be a pertinent but often undetected pathophysiology in this population ( 7 ). Moreover, it is generally appreciated that injury to the brain resulting from trauma often arises globally, as axons crossing areas of differing tissue density react differently to the mechanical force of the trauma ( 8 ). This can cause widespread damage, which may be explored using network and connectivity analyses that draw directly upon anatomically accurate estimations of white matter connection density. A connectomic network approach allows data integration of distinct regions of brain anatomy and connection strength, which makes it a useful methodology for examining both localized and global effects of mTBI on white matter ( 9 ).

In this case study, unique due to the rare availability of pre- and post-injury 7 Tesla high-resolution data, we investigated the trajectory of structural changes attributed to mTBI. The multiple time-point pre-injury data is an uncommon strength to the present research, as longitudinal data can be examined in both healthy and post-injury settings. Moreover, we aimed to investigate and characterize disparities between conventional structural MRI and diffusion-weighted connectomic findings. In this report, we hope to illustrate how recent developments in the field of computational neuroimaging, such as morphometric subcortical segmentations and network theory, may aid in the identification of suitably sensitive biomarkers of brain injury.

Case Description

A 38-year-old female was involved in a motor vehicle accident in which she was a pedestrian hit by a car turning into the intersection she was crossing. She was thrown across the road where her head hit the curb. She was transported to the nearby hospital where acutely, the patient was dizzy, faint and mildly confused. Head CT revealed subcutaneous soft tissue swelling over the right parietal bone. There was no evidence of acute territorial infarction or intracranial hemorrhage. Ventricles and sulci appeared normal in size and configuration for the patient's age. There was no midline shift or other mass effect, and gray-white matter differentiation was maintained throughout the brain. The patient received surgical staples to close a laceration over the right parietal bone and was discharged home. The patient reported minimal headaches or nausea, but dizziness, daytime fatigue, hypersomnolence, reduced problem-solving skills and slowed cognitive processing persisted for several weeks following the injury. She returned to work the day after the injury, working slightly reduced hours to accommodate fatigue. Full recovery, defined as full symptom resolution and return to baseline function, was achieved ~6 months post-injury. The patient gave fully informed consent for participation in the presented research. Institutional Review Board (IRB) approval for human research was obtained for this experiment from the Program for Protection of Human Subjects at the Icahn School of Medicine at Mount Sinai.

Longitudinal Data Acquisition

The patient had undergone two scanning sessions at 7 Tesla prior to the head injury as a healthy control. Two more scans were acquired post-injury. The scan times in relation to injury were as follows: 23 months prior to injury, 12 months prior to injury, 2 weeks post-injury, and 8 months post-injury. All MRI scanning was performed using the same Siemens 7T scanner. Clinical assessment and an initial head CT were carried out immediately after injury, and neurocognitive testing was administered by a trained clinician 18 months post-injury.

Clinical Neurocognitive Data Acquisition

A brief battery of performance-based neurocognitive tests was administered to estimate premorbid intellectual functioning and quantitatively confirm cognitive recovery 18 months post-injury ( 10 ).

MRI Acquisition

Included in each MRI protocol was a T 1 -weighted Magnetization Prepared 2 Rapid Acquisition Gradient Echo (MP2RAGE) ( 11 ), a T2-weighted Turbo Spin Echo (TSE), and a diffusion MRI.

The MP2RAGE sequence obtains improved gray-white contrast at high field compared to the classic MPRAGE acquisition ( 11 ). High spatial resolution voxel size was 0.8 mm isotropic, TR/TE = 6,000/3.2 ms, TI1(θ 1 )/TI2(θ 2 ) = 1,050(5°)/ 3,000(4°) ms and total acquisition time was 7:26 minutes. From the MP2RAGE dataset, a total of four images were reconstructed from (a) data acquired after inversion time (TI) of 1,050 ms, (b) data acquired after TI of 3,000 ms, (c) T1 relaxation maps calculated from (a) and (b), and (d) uniform-denoised (UNIDEN) images calculated from (a) and (b). An in-plane acceleration factor of 3 was used.

Two TSE structural images were obtained at high in-plane resolution (0.4 × 0.4 mm 2 ), a slice thickness of 2 mm, TR/TE = 6,900/69 ms and θ = 150°. An in-plane acceleration factor of 2 was used. The first T2-TSE was obtained with a 6:14 min acquisition time in a coronal-oblique orientation where the imaging plane was aligned perpendicular to the long axis of the hippocampus. The second T2-TSE was obtained in an axial orientation; the imaging plane alighted along the axis connecting the anterior commissure and the posterior commissure (AC-PC). The acquisition time for the second T2-TSE scan was 6:50 min.

Diffusion MRI data were collected using a single-shot spin-EPI sequence aligned axially with an isotropic resolution of 1.05 mm, an in-plane acceleration factor of 3, a multi-band acceleration factor of 2 and TR/TE = 6,900/67 ms. The diffusion sequence was a paired acquisition with reversed phase encoding in the AP/PA direction, and each pair had 64 diffusion encoding directions ( b = 1,200 s/mm 2 ) and 4 unweighted scans ( b = 0 s/mm 2 ). Total scan time for the paired acquisition was 20 min.

Structural MRI Analysis

The FreeSurfer “recon-all” pipeline (version 6.0) ( 12 ) was used to carry out the following processing steps on T1-weighted structural data: motion correction, intensity correction, transform to Talairach space, intensity normalization, skull strip, subcortical segmentation, neck remove, subcortical labeling, segmentation statistics, a second intensity correction using brain only (after skull strip), white matter segmentation, subcortical mass creation, brain surface creation, surface inflation, automatic topology fixer, cortical thickness/pial surfaces, cortical ribbon mask, spherical inflation of the brain surface, ipsilateral surface registration, contralateral surface registration, resampling of the average atlas curvature to subject, cortical parcellation, and creation of summary table for parcellation statistics. As the T1-weighted data had a submillimeter isotropic voxel size, the “-hires” flag was used to preserve enhanced spatial resolution ( 13 ).

Hippocampal subfield ( 14 ) and amygdala subnuclei segmentation ( 15 ) was carried out using FreeSurfer 6.0 development version. A multi-spectral approach was used, utilizing both the T1-weighted and T2-weighted images, leveraging the enhanced resolution of the T2-weighted image to provide additional anatomical information. This subcortical segmentation is visualized in Figure 1 .

Figure 1. (a) Multi-spectral hippocampal subfield segmentation (CA1, CA3, CA4, dentate gyrus, and subicular complex) with underlay of axial T1-weighted data. (b) Hippocampal subfield segmentation with underlay of axial T2-weighted data. (c) Multi-spectral amygdala subnuclei segmentation (lateral, basal, accessory basal, central, cortical, medial nuclei, and corticoamygdaloid transition area (CATA) with underlay of axial T1-weighted data. (d) Amygdala subnuclei segmentation with underlay of axial T2-weighted data.

To investigate test–rest variability, blinded re-runs of the imaging processing were carried out, and pairwise coefficients of variation were calculated per measure by dividing the standard deviation by the mean and multiplying by 100 to produce a percentage.

Diffusion MRI Analysis

Denoising of the diffusion weighted data was performed using MRTrix two-shell phase-reversed processing ( 16 , 17 ). Segmented and parcellated structural images from the FreeSurfer “recon-all” pipeline were used for whole brain masking ( 12 ). B 1 field inhomogeneity correction was carried out ( 18 ) and the fiber orientation distributions (FODs) were created from the diffusion data using constrained super-resolved spherical deconvolution ( 19 ). Estimation of the diffusion tensor was done using iteratively reweighted linear least squares methodology ( 20 ). The tensor image was used to create a whole brain map of fractional anisotropy (FA) ( 21 ). Mean FA was extracted from cerebral white matter hemispheric masks created by the FreeSurfer pipeline.

Co-registration of anatomical images into diffusion space was then carried out using Statistical Parametric Mapping software (SPM12). Degree of spline interpolation was 4. The MRTrix command “5ttgen” was used to generate a five tissue-type segmentation image, utilizing the FreeSurfer outputs, to use in anatomically constrained tractography ( 22 ). A segmented mask image was then created for the seeding of tractography streamlines at the gray-white matter interface ( 22 ). The fiber orientation distributions were then used to create whole brain tractograms for each participant ( 23 ). Ten million streamlines were generated from the probabilistic tractography per brain. Individual step size for the streamlines was 0.1 mm × voxel size, the fiber orientation distribution amplitude cut-off was 0.05 and the maximum angle between successive steps was 90° × step size × voxel size. Seeds were placed in the gray white matter interface. Spherical deconvolution informed filtering (SIFT2) was applied to the tractograms, the purpose of which was to weight streamlines based on likelihood of anatomical accuracy, remove spurious streamlines from further analysis and ensure data that is highly representative of ground-truth biology ( 24 ). A structural connectome, based on node-to-node connection density, was created using MRTrix ( 25 ).

Structural connectomes at different timepoints were compared by custom functions that performed elementwise subtraction of the matrices in MATLAB. Similarly, variability of the connectomes was assessed by stacking matrices into a 3-dimensional array and computing the mean and standard deviation along the z -axis for each network edge. To investigate variability between scan timepoints, specifically to examine test–retest variation, the two pre-injury connectomes were compared using co-efficients of variation, calculated elementwise for each edge of the connectivity matrix by dividing the standard deviation by the mean and multiplying by 100 to produce a percentage. The mean co-efficient of variation was calculated by averaging the co-efficients of variation across the whole matrix. To determine a streamline threshold of the connectome with an acceptable level of variability, mean matrix co-efficients of variation were calculated for the following streamline thresholds: 25, 50, 100, 200, 400, 800, 1,600, 3,200, 6,400, 12,800, and 25,600. Actual streamline thresholding was subsequently set at 15,000, discarding edges consisting of streamline bundles with less density than the threshold.

Clinical Neurocognitive Data

This high-achieving woman with a history of academic excellence throughout 20 years of formal education had an estimated premorbid intellectual ability in the high average-superior range ( 10 ). At the time of testing her performance-based intellectual quotient (IQ) was in the superior range, consistent with expectation. She demonstrated a relative strength in verbal comprehension (96th percentile) as compared to perceptual reasoning (>99th percentile). Performance on tests of contextual and non-contextual verbal memory was consistently above the 98th percentile, while visual memory performance was at the 34th percentile (Average range). Tests of complex attention and working memory were generally above the 85th percentile (High Average range), and tests of verbal fluency were variable (semantic fluency 38th percentile; phonemic fluency 96th percentile). Performance on timed tests of sequencing and task-switching were below expectation (<1st percentile – 62nd percentile) while untimed tests of these higher order executive functions were well within expectation (>96th percentile). Overall, performance on neurocognitive tests indicate superior intellectual ability with performance 18 months post-TBI largely consistent with expectations; impaired performance on select timed tests suggest a tendency to sacrifice speed to ensure accuracy which may reflect a compensatory strategy.

The subcortical segmentation of the amygdala nuclei and hippocampal subfields did not reveal any clear changes between the scanning timepoints. The average test-retest coefficient of variation for the hippocampal subfield segmentation was 1.4%, and 5.2% for the amygdala nuclei. Considering this estimation of variability within the image processing, the data did not reveal evidence of volumetric change to the hippocampus or amygdala post-injury, either at a whole or substructure level.

At post-injury timepoint 1, the right and left hemispheric brain segmentation revealed lower cortical gray matter and cerebral white matter volume compared to other scanning timepoints. No change was apparent in ventricle volume ( Figure 2 ). The average test-retest variation co-efficient for each of these variables was <0.001%, significantly less than the observed change post-TBI.

Figure 2 . Volumes of choroid ventricles, hemispheric gray matter, and hemispheric white matter and fractional anisotropy of the hemispheric cerebral white matter across scanning timepoints.

Concurrent with the changes in the structural volumetrics, FA of the cerebral white matter was markedly reduced in both hemispheres in the first scan following the head trauma. In both the left and right hemispheres, the final timepoint scan revealed a subsequent increase of FA to levels similar to those pre-injury ( Figure 2 ).

Averaging of the structural diffusion MRI connectomes pre- and post-injury revealed a widespread decrease in connectivity after the patient’s head trauma, mainly involving connections between cortical regions ( Figure 3A ). To a lesser degree, mean pre- to post-injury comparison also revealed some increased connectivity, primarily in subcortical areas and the forebrain ( Figure 3B ). A comparison between the first and second post-injury connectome matrices was then carried out, to investigate if changes to the patient's structural connectivity post-TBI were consistent over time. The results showed that at post-injury timepoint 1, connection density was extensively reduced, but this decrease in connectivity was partially reduced by post-injury timepoint 2.

Figure 3. (a) Areas of decreased connection density of the structural network mean post-injury compared to mean pre-injury. (b) Regions of increased connection density of the structural network mean post-injury compared to mean-pre-injury.

Structural high-resolution neuroimaging data in this mTBI case study revealed reduced cerebral white matter and cortical gray matter volumes post-injury that appeared to restore to pre-injury quantifications by the 8-month post-TBI MRI acquisition. Whole brain white matter fractional anisotropy demonstrated a concurrent pattern of change, with marked short-term reductions post-injury returning to a baseline level by the 8-month following TBI. The structural connectome, derived from tractography-based connection density metrics, showed that post-injury connectivity was reduced extensively between cortical nodes, in particular in the right parietal injury site. To a lesser degree, limbic, and forebrain regions were intra-hyperconnected. The final post-injury scanning timepoint showed globalized increases in connection density compared to the primary post-injury data, suggestive of recovery of the network. We highlight here the rare availability of pre-injury data, which gives significant benefits to interpretability compared to post-injury only research into mTBI.

Interestingly, subcortical segmentation and analysis of regional brain volumetrics did not reveal changes post-TBI, indicating a robustness of the limbic structures in this case. This is in contrast to previous reports of the hippocampal and amygdala structures being promising predictors of outcome when analyzed at a gross level ( 26 ); however, severity and type of TBI are significant contributors to heterogeneity. Our results suggest that subcortical volumetrics may not be a sensitive measure of mTBI pathology in all cases. At a whole brain level, it appeared that quantification of hemispheric white and gray matter volumes was a more effective metric of brain changes post-TBI in this case, especially when considering the minimal test–retest variability.

The mechanical properties of the white matter make it particularly vulnerable to injury in TBI ( 5 ), which was a prominent motivation for the use of high spatial resolution diffusion-weighted MRI in this case investigation. Our findings show that primary post-injury connectivity is reduced in a widespread manner, mainly between cortical nodes. Similarly, a study of mTBI patients and matched controls revealed decreased fractional anisotropy in the association, commissural and projection white matter tracts, indicative of reduced connectivity, which partially resolved 6 months post-injury ( 27 ). In addition, our results identified increased white matter connectivity in the limbic and forebrain regions post-injury compared to pre-injury data. Resting-state investigation into mTBI has shown comparable hyperconnectivity in the limbic system post-injury ( 28 ), and thalamic circuitry, in particular, has been shown to be a key underlying factor in mTBI recovery ( 29 ). Alterations of the connectome post-mTBI in the present case were further substantiated by widespread corroborative changes in fractional anisotropy, suggesting that after injury, white matter microstructure was changed in a way highly indicative of axonal damage ( 30 ).

The involvement of the posterior cingulate, precuneus and prefrontal cortices in the decline of structural network density implicates decreased cohesiveness of the default mode system. Decreased functional coupling of the default mode network, in particular in the frontal regions, has been shown to occur during sleep in both humans and primates, suggesting that default mode cohesiveness may be required to maintain conscious states ( 31 ). The default mode white matter damage detected in the present study; therefore, may be a contributory factor in the patient's reported hypersomnolence and fatigue post-injury. Similarly, the superior frontal and orbitofrontal regions are integral sites for executive processing ( 32 ), and the observed decreases in network connection density here tally closely with the patient's symptomology of delayed cognitive processing post-injury. Decreased white matter connectivity of the insular cortex implicates decreased salience network integration, which may be another possible physiological correlate of cognitive slowing via diminished attentional regulation ( 33 ). Alterations to the thalamic-occipital lobe circuitry, which forms the posterior portion of the primary visual pathway ( 34 ), may also underlie symptomatic dizziness post-TBI. Localized increases in the mean connection density of the thalamus post-injury, a factor previously reported as a protective feature against long-term pathological effects in mTBI ( 29 ), is also seen in this patient, and may reflect compensatory plasticity promoting sensory relay and upstream network integration ( 35 ).

Additionally, consistent with previous studies of diffusion-weighted imaging in TBI ( 27 ), the current data show recovery of the brain white matter, which is consistent with symptomatic recovery per subjective report at ~6 months post-injury. However, the structural network at post-injury timepoint 2 exhibits some evidence for enduring TBI-related change, given that compared to pre-injury data, the final timepoint connectivity shows some disorganization, albeit markedly improved compared to the initial post-injury network. The functional implications of long-term reorganization of the network appear to be minimal, although may account for the few isolated areas of cognitive performance that were lower than expected in patient at 18 months post-injury. Taken together, our findings show that diffusion MRI connectomics and microstructural measurements may be sensitive to clinical status.

This 7 Tesla case report demonstrates novel evidence of widespread connectivity and microstructural changes at a highly granular level after mTBI, where conventional neuroimaging at a clinical level showed no radiological abnormalities. Moreover, we demonstrate the disparity between T1- and T2-weighted acquisition-derived information and diffusion MRI and suggest that diffusion-weighted investigation of TBI symptomology may be of significant use in clinical practice.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Regional ethics committee, Icahn School of Medicine at Mount Sinai. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

SB, KD-O'C, PB, and RF contributed substantially to the study conception and design, drafted, and revised the article for important intellectual content and gave final approval of the version to be published. EW provided guidance for cognitive assessment. SB carried out all data processing and neuroimage analyses. All authors contributed to the article and approved the submitted version.

This work was funded by DOD-IDA W81XWH-19-1-0616 and NIH R01 MH109544.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: 7T MRI, diffusion MRI, traumatic brain injury, structural connectivity, case study

Citation: Brown SSG, Dams-O’Connor K, Watson E, Balchandani P and Feldman RE (2021) Case Report: An MRI Traumatic Brain Injury Longitudinal Case Study at 7 Tesla: Pre- and Post-injury Structural Network and Volumetric Reorganization and Recovery. Front. Neurol. 12:631330. doi: 10.3389/fneur.2021.631330

Received: 19 November 2020; Accepted: 15 April 2021; Published: 17 May 2021.

Reviewed by:

Copyright © 2021 Brown, Dams-O'Connor, Watson, Balchandani and Feldman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Stephanie S. G. Brown, sb2403@medschl.cam.ac.uk

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Successful outcome in severe traumatic brain injury: a case study

Affiliation.

• 1 Neurotrauma Intensive Care Unit, Hospital of the University of Pennsylvania in Philadelphia, PA, USA.
• PMID: 16379129
• DOI: 10.1097/01376517-200510000-00002

This case study describes the management of a 54-year-old male who presented to the Hospital of the University of Pennsylvania (HUP) with a traumatic brain injury (TBI) after being assaulted. He underwent an emergent bifrontal decompressive hemicraniectomy for multiple, severe frontal contusions. His postoperative course included monitoring of intracranial pressure, cerebral perfusion pressure, partial pressure of brain oxygen, brain temperature, and medical management based on HUP's established TBI algorithm. This case study explores the potential benefit of combining multimodality monitoring and TBI guidelines in the management of severe TBI.

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Traumatic Brain Injury Rehabilitation Case Study

• First Online: 23 May 2019

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• Samantha L. Backhaus 2 &
• Ana Durand-Sanchez 3  

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TBI rehabilitation case is a case example of traumatic brain injury (TBI) using neuropsychological (NP) and neurological evaluations and follow-ups to assist the patient in helping her reach her long-term rehabilitation goals. The largest obstacles to achieving success included the patient’s defensiveness and psychological reactions to her situation, fatigue, and higher-level cognitive challenges which were detected on neuropsychological assessment.

Key Questions

What are the benefits of conducting a neuropsychological examination, and how can this data be used to help the physiatrist manage the overall care?

How can the neuropsychologist and physiatrist collaborate effectively to help the patient reach her neurorehabilitation goals?

This case study helps to demonstrate the value of NP testing in treatment of TBI, as well as the value of establishing a strong collaborative relationship between the neuropsychologist and the physiatrist specializing in brain injury rehabilitation.

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Neuropsychological Rehabilitation After Traumatic Brain Injury

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Ana Durand-Sanchez

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Karen M. Sanders

Chapter Review Questions

In this case, the diagnosis of TBI was already known. Given this information, how was it useful to obtain neuropsychological testing data, and how did this guide the treatment recommendations for the patient’s outpatient treatment, particularly given that she appeared so “high-level” and obtained some “average” scores?

Why was it important to recommend that a more comprehensive neuropsychological test be given as opposed to providing a basic mental status examination for this patient? Are there times when administering mental status examinations can be useful?

Why was it important for the physiatrist to have some knowledge on treatment of mood disturbances, and when would it have been a good idea to refer out to a psychiatrist?

How does collaboration with a neuropsychologist help a physician better monitor a patient’s cognitive progress and rehabilitation process? How does it help synergize the management of mood disturbances?

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Backhaus, S.L., Durand-Sanchez, A. (2019). Traumatic Brain Injury Rehabilitation Case Study. In: Sanders, K. (eds) Physician's Field Guide to Neuropsychology. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8722-1_26

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Researchers reveal how trauma changes the brain

Exposure to trauma can be life-changing – and researchers are learning more about how traumatic events may physically change our brains. But these changes are not happening because of physical injury, rather our brain appears to rewire itself after these experiences. Understanding the mechanisms involved in these changes and how the brain learns about an environment and predicts threats and safety is a focus of the  ZVR Lab  at the  Del Monte Institute for Neuroscience at the University of Rochester , which is led by assistant professor  Benjamin Suarez- Jimenez, Ph.D.

“We are learning more about how people exposed to trauma learn to distinguish between what is safe and what is not. Their brain is giving us insight into what might be going awry in specific mechanisms that are impacted by trauma exposure, especially when emotion is involved,” said Suarez-Jimenez, who began this work as a post-doctoral fellow in the lab of Yuval Neria, Ph.D., professor at Columbia University Irving Medical Center.

Their research, recently published in  Communications Biology , identified changes in the salience network – a mechanism in the brain used for learning and survival – in people exposed to trauma (with and without psychopathologies, including PTSD, depression, and anxiety). Using fMRI, the researchers recorded activity in the brains of participants as they looked at different-sized circles – only one size was associated with a small shock (or threat). Along with the changes in the salience network, researchers found another difference – this one within the trauma-exposed resilient group. They found the brains of people exposed to trauma without psychopathologies were compensating for changes in their brain processes by engaging the executive control network – one of the dominate networks of the brain.

“Knowing what to look for in the brain when someone is exposed to trauma could significantly advance treatments,” said Suarez-Jimenez, a co-first author with Xi Zhu, PhD, Assistant Professor of Clinical Neurobiology at Columbia, of this paper. “In this case, we know where a change is happening in the brain and how some people can work around that change. It is a marker of resilience.”

Adding the element of emotion

The possibility of threat can change how someone exposed to trauma reacts – researchers found this is the case in people with  post-traumatic stress disorder (PTSD) , as described in a recent study in  Depression & Anxiety . Suarez-Jimenez, his fellow co-authors, and senior author Neria found patients with PTSD can complete the same task as someone without exposure to trauma when no emotion is involved. However, when emotion invoked by a threat was added to a similar task, those with PTSD had more difficulty distinguishing between the differences.

The team used the same methods as the other experiment – different circle sizes with one size linked to a threat in the form of a shock. Using fMRI, researchers observed people with PTSD had less signaling between the hippocampus – an area of the brain responsible for emotion and memory – and the salience network – a mechanism used for learning and survival. They also detected less signaling between the amygdala (another area linked to emotion) and the default mode network (an area of the brain that activates when someone is not focused on the outside world). These findings reflect a person with PTSD’s inability to effectively distinguish differences between the circles.  

“This tells us that patients with PTSD have issues discriminating only when there is an emotional component. In this case, aversive; we still need to confirm if this is true for other emotions like sadness, disgust, happiness, etc.,” said Suarez-Jimenez. “So, it might be that in the real-world emotions overload their cognitive ability to discriminate between safety, danger, or reward. It overgeneralizes towards danger.”

“Taken together, findings from both papers, coming out of a NIMH funded study aiming to uncover neural and behavioral mechanisms of trauma, PTSD and resilience, help to extend our knowledge about the effect of trauma on the brain,” said Neria, lead PI on this study. “PTSD is driven by remarkable dysfunction in brain areas vital to fear processing and response. My lab at Columbia and the Dr. Suarez-Jimenez lab at Rochester are committed to advance neurobiological research that will serve the purpose of development new and better treatments that can effectively target aberrant fear circuits.”

Suarez-Jimenez will continue exploring the brain mechanisms and the different emotions associated with them by using more real-life situations with the help of virtual reality in his lab. He wants to understand if these mechanisms and changes are specific to a threat and if they expand to context-related processes.

Additional authors include co-first authors John Keefe, Ph.D., of Albert Einstein College of Medicine and Xi Zhu, Ph.D., of Columbia University Irving Medical Center, Amit Lazarov, Ph.D., of Columbia University Irving Medical Center, Ariel Durosky of the University of Tulsa, Oklahoma, Sara Such of the University of Pennsylvania, Caroline Marohasy of the University of Washington, Seattle, and Shmuel Lissek of the University of Minnesota, Minneapolis. The research was supported by the National Institute of Mental Health.

Additional authors on the  Communications Biology  paper include co-first author Xi Zhu, Ph.D., Amit Lazarov, Ph.D., Scott Small, M.D., of Columbia University Irving Medical Center, Ariel Durosky of the University of Tulsa, Oklahoma, Sara Such of the University of Pennsylvania, Caroline Marohasy of the University of Washington, Seattle, Tor Wager, Ph.D., of Dartmouth College, Martin Lindquist, Ph.D. of Johns Hopkins, and Shmuel Lissek, Ph.D., of the University of Minnesota. The research was supported by the National Institute of Mental Health.

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Clinical Outcomes After Traumatic Brain Injury and Exposure to Extracranial Surgery : A TRACK-TBI Study

• 1 Department of Anesthesiology, Medical College of Wisconsin, Milwaukee
• 2 Department of Anesthesiology, Zablocki Veterans Affairs Medical Center, Milwaukee, Wisconsin
• 3 Department of Neurological Surgery, University of Washington, Seattle
• 4 Department of Biostatistics, University of Washington, Seattle
• 5 Department of Neurosurgery, Baylor College of Medicine, Houston, Texas
• 6 Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas
• 7 Department of Neurological Surgery, University of California, San Francisco
• 8 Brain and Spinal Injury Center, San Francisco, California
• 9 Department of Neurosurgery, Medical College of Wisconsin, Milwaukee
• 10 Department of Neurology, Medical College of Wisconsin, Milwaukee
• Invited Commentary Surgery, Anesthesia, and TBI Outcomes Caitlin R. Collins, MD, MPH; Andre Campbell, MD JAMA Surgery

Question   Are functional and cognitive outcomes worse after traumatic brain injury (TBI) in those exposed to extracranial surgery and anesthesia compared with unexposed people?

Findings   In this cohort study of 1835 level I trauma center patients with broad TBI severity levels, extracranial surgery and anesthesia were associated with worse functional and cognitive outcomes at 2 weeks and 6 months after injury, especially in persons with acute intracranial findings on neuroimaging.

Meaning   The findings support an association between exposure to extracranial surgery and anesthesia and worse outcomes after TBI in both functional and cognitive domains, suggesting that the perioperative period may be associated with detrimental effects on an injured brain.

Importance   Traumatic brain injury (TBI) is associated with persistent functional and cognitive deficits, which may be susceptible to secondary insults. The implications of exposure to surgery and anesthesia after TBI warrant investigation, given that surgery has been associated with neurocognitive disorders.

Objective   To examine whether exposure to extracranial (EC) surgery and anesthesia is related to worse functional and cognitive outcomes after TBI.

Design, Setting, and Participants   This study was a retrospective, secondary analysis of data from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study, a prospective cohort study that assessed longitudinal outcomes of participants enrolled at 18 level I US trauma centers between February 1, 2014, and August 31, 2018. Participants were 17 years or older, presented within 24 hours of trauma, were admitted to an inpatient unit from the emergency department, had known Glasgow Coma Scale (GCS) and head computed tomography (CT) status, and did not undergo cranial surgery. This analysis was conducted between January 2, 2020, and August 8, 2023.

Exposure   Participants who underwent EC surgery during the index admission were compared with participants with no surgery in groups with a peripheral orthopedic injury or a TBI and were classified as having uncomplicated mild TBI (GCS score of 13-15 and negative CT results [CT − mTBI]), complicated mild TBI (GCS score of 13-15 and positive CT results [CT + mTBI]), or moderate to severe TBI (GCS score of 3-12 [m/sTBI]).

Main Outcomes and Measures   The primary outcomes were functional limitations quantified by the Glasgow Outcome Scale–Extended for all injuries (GOSE-ALL) and brain injury (GOSE-TBI) and neurocognitive outcomes at 2 weeks and 6 months after injury.

Results   A total of 1835 participants (mean [SD] age, 42.2 [17.8] years; 1279 [70%] male; 299 Black, 1412 White, and 96 other) were analyzed, including 1349 nonsurgical participants and 486 participants undergoing EC surgery. The participants undergoing EC surgery across all TBI severities had significantly worse GOSE-ALL scores at 2 weeks and 6 months compared with their nonsurgical counterparts. At 6 months after injury, m/sTBI and CT + mTBI participants who underwent EC surgery had significantly worse GOSE-TBI scores (B = −1.11 [95% CI, −1.53 to −0.68] in participants with m/sTBI and −0.39 [95% CI, −0.77 to −0.01] in participants with CT + mTBI) and performed worse on the Trail Making Test Part B (B = 30.1 [95% CI, 11.9-48.2] in participants with m/sTBI and 26.3 [95% CI, 11.3-41.2] in participants with CT + mTBI).

Conclusions and Relevance   This study found that exposure to EC surgery and anesthesia was associated with adverse functional outcomes and impaired executive function after TBI. This unfavorable association warrants further investigation of the potential mechanisms and clinical implications that could inform decisions regarding the timing of surgical interventions in patients after TBI.

• Invited Commentary Surgery, Anesthesia, and TBI Outcomes JAMA Surgery

Read More About

Roberts CJ , Barber J , Temkin NR, et al. Clinical Outcomes After Traumatic Brain Injury and Exposure to Extracranial Surgery : A TRACK-TBI Study . JAMA Surg. 2024;159(3):248–259. doi:10.1001/jamasurg.2023.6374

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• Study protocol
• Open access
• Published: 17 November 2020

Case management after acquired brain injury compared to care as usual: study protocol for a 2-year pragmatic randomized controlled superiority trial with two parallel groups

• Annemarie P. M. Stiekema   ORCID: orcid.org/0000-0002-6739-3772 1 , 2 ,
• Christine Resch 2 , 3 ,
• Mireille Donkervoort 4 ,
• Natska Jansen 5 , 6 ,
• Kitty H. M. Jurrius 4 ,
• Judith M. Zadoks 7 , 8 &
• Caroline M. van Heugten 1 , 2 , 3  

Trials volume  21 , Article number:  928 ( 2020 ) Cite this article

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Metrics details

People with acquired brain injury may suffer from cognitive, emotional and behavioural changes in the long term. Continuity of care is often lacking, leading to a variety of unmet needs and hindering psychosocial functioning from the occurrence of brain injury up to years thereafter. Case management aims to prevent (escalation of) problems and to facilitate timely access to appropriate services. In other populations, case management has shown to improve psychosocial well-being. In this study, we aim to evaluate the feasibility of case management after acquired brain injury and its effectiveness and cost-effectiveness, compared to care as usual.

This is a pragmatic randomized controlled superiority trial with two parallel groups and repeated measures in adults with ABI and their family, taking place between November 2019 and December 2021 in three provinces in the Netherlands. Participants will be randomly allocated to either the case management group, receiving case management from hospital discharge up to 2 years thereafter, or the control group, receiving care as usual. Effectiveness will be evaluated every 6 months for 18–24 months by patient-reported psychosocial well-being (Hospital Anxiety and Depression Scale (HADS), Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P) restriction subscale and the Life Satisfaction Questionnaire (LiSat)), self-management (Patient Activation Measure (PAM)) and care needs (Longer-term Unmet Needs after Stroke (LUNS)). Family outcomes include self-efficacy (Carer Self-Efficacy Scale (CSES)), caregiver burden (Caregiver Strain Index (CSI)), psychosocial well-being (LiSat, HADS), family needs (Family Needs Questionnaire (FNQ)). Feasibility will be evaluated using qualitative methods, assessing fidelity, dose delivered, dose received, reach, recruitment and context. Cost-effectiveness will be determined by the EQ-5D-3L and service use.

At the moment, there is no integrated health care service for people with acquired brain injury and their family members in the long term. If case management is shown to be feasible and (cost)-effective, it could bridge the gap between patients’ and families’ needs and the available services.

Trial registration

Netherlands Trial Register NL8104 . Registered on 22 October 2019.

Peer Review reports

Administrative information

The order of the items has been modified to group similar items (see http://www.equator-network.org/reporting-guidelines/spirit-2013-statement-defining-standard-protocol-items-for-clinical-trials/ ).

Introduction

Background and rationale {6a}.

Acquired brain injury (ABI) may result from stroke (e.g. ischemic or haemorrhagic disruption of blood flow), traumatic brain injury (e.g. from a fall or a blow to the head), brain disease or hypoxia (e.g. after cardiac arrest or near-drowning). People with ABI often experience physical, communicative, cognitive, emotional or behavioural problems [ 1 , 2 , 3 ]. The persisting nature of these changes poses day-to-day challenges in a variety of life domains, such as work or education, household, social relationships and leisure [ 4 , 5 , 6 ], affecting not only quality of life of people with ABI but that of family members as well, as they may need to take on the role of caregiver [ 3 , 7 , 8 ]. There are ample health care services available for people with ABI, but problems exist with regard to their continuity, accessibility and timing [ 9 , 10 , 11 ]. People with ABI and family members feel ill-prepared for discharge from the hospital or rehabilitation centre and ‘abandoned’ once at home, being left with unmet health, social and vocational needs in the long term [ 10 , 11 ].

The importance of supporting a changed life after ABI is increasingly recognized in clinical guidelines [ 12 , 13 ]. Since there are relatively few methodologically sound studies evaluating longer-term care, the Action Plan for Stroke in Europe and the World Stroke Organization state that the development and evaluation of a ‘seamless, coordinated chain of support’, which includes life after ABI, is a development and research priority [ 14 , 15 ].

The development and research on longer-term care services for ABI falls behind in comparison to populations where the need for long-term support has been recognized for longer, such as dementia, oncology and diabetes. A form of longer-term support for these populations is case management, which focusses on supporting people to adapt to the consequences of their health condition in daily life [ 16 , 17 , 18 , 19 , 20 ]. Case management promotes self-management, which refers to choosing strategies, making decisions and undertaking activities to manage a long-term condition and its consequences [ 21 ]. Case management varies in form and duration. The key element is a professional, the case manager, who serves as a first point of contact for patients and their family, is familiar with their situation, supports independent living and links them to available services in the community [ 22 ]. Case management has a positive impact on well-being in dementia, oncology and diabetes, reducing anxiety and depression and increasing quality of life [ 16 , 17 , 18 , 19 , 20 ]. It may decrease financial strains on healthcare as well; for dementia, costs were reduced by 22–33% when providing case management compared to care as usual [ 23 ].

Case management for ABI has been described in the literature, and commonly involves engagement, assessment, planning, education, training and skills development, emotional and motivational support, advising, coordination and monitoring [ 24 ]. These elements are based on best practice; to the best of our knowledge, no randomized controlled trials on long-term case management for ABI have been undertaken to date. The evidence base so far is weak, with a few relatively old non-randomized studies on case management for traumatic brain injury of too low a quality to draw conclusions on its effectiveness [ 25 , 26 , 27 , 28 , 29 ]. Long-term follow-up did show a positive effect on social activities and depression in stroke in a non-randomized trial [ 30 ] and short-term transitional care interventions also show promising results [ 31 , 32 ]. However, since learning how to live with ABI is a dynamic process with fluctuating needs over the course of several years, 3 to 6-month follow-up in the first year and annual reviews hereafter are necessary [ 14 ]. A methodologically sound investigation of the feasibility and effects in terms of health and costs of such long-term support is called for [ 14 , 15 ]. This article describes the study protocol for a pragmatic randomized controlled trial on long-term case management (18–24 months) for people with ABI and their family.

Objectives {7}

The primary objective is to examine the effectiveness of case management for ABI compared to the care as usual on psychosocial well-being (emotional, participation and quality of life outcomes), self-efficacy and unmet needs. Secondary objectives are to explore cost-effectiveness and cost-utility (the balance of costs and gains in health and well-being) of case management compared to care as usual and to explore feasibility of case management for people with ABI and their family in terms of fidelity, dose delivered, dose received, reach and recruitment within its physical, social and political context. We expect case management to be effective and feasible, and we hypothesize that healthcare costs will rise at first and will be reduced in the long-term.

Trial design {8}

This is a pragmatic prospective randomized controlled superiority trial with two parallel groups and repeated measures. Randomization will be performed as block randomization with a 1:1 allocation.

Methods: participants, interventions and outcomes

Study setting {9}.

Recruitment of people with ABI will take place between November 2019 and July 2020 in three hospitals in the Netherlands: Deventer hospital (Deventer), St. Antonius hospital (Nieuwegein and Woerden) and Flevo hospital (Almere). Hospital staff will recruit people with ABI without further involvement in the study procedures, assessments will take place through home visits, via telephone or by sending questionnaires via mail.

Eligibility criteria {10}

People with abi.

People with ABI are eligible for the trial if they comply with all of the following criteria at hospital discharge:

Acquired brain injury objectified by medical specialist (meningitis, encephalitis, hydrocephalus, subarachnoid hemorrhage, intracerebral or intracranial hemorrhage, ischemic stroke, transient ischemic attack, concussion, contusion, other head trauma).

Aged 18 years or older.

Living in the community prior to the injury.

Discharged home or to a rehabilitation centre after hospital visit/admission.

Sufficient command of the Dutch language to understand study procedures.

Access to a computer and the internet (to use the monitoring tool, see ‘ Intervention description {11a} ’).

Willing and able to give informed consent.

Exclusion criteria for people with ABI are:

A neurodegenerative disorder such as Parkinson’s disease or dementia (because of the progressive course of the disease).

A diagnosis related to neuro-oncology (since an intensive care trajectory is already in place for this group).

Discharge to a nursing home.

Family members

Family members are eligible when they comply with all of the following below. Off note, we speak of family members since usually the partner, a child or a parent is most likely to be the primary caregiver in case the person with ABI needs support, but friends or neighbours can also participate in this role if they are the ones most close to the person with ABI.

The person with ABI is eligible and willing to participate (i.e. family members can only participate if their relative with ABI is participating).

They are (or would be if necessary) the primary informal caregiver; i.e. the person most close the person with ABI.

Access to a computer and the internet (to use the monitoring tool and questionnaires).

Case managers

Health care professionals are eligible for the role of case manager if they have professional experience in caring for people with ABI at bachelors’ level or higher (e.g. social workers, nurses, speech and language therapists and occupational therapists). They need to be available for at least 4–8 h per week for the duration of the project, willing to participate in the case manager training at the beginning of the project and in the monthly supervision meetings, and willing to register and document their case manager activities and experiences for research purposes. A formal application procedure will be followed; candidates are hired based on their resume, motivation and job interview by the project leaders.

Who will take informed consent? {26a}

People with ABI and family members who are willing to participate will be visited at home by a trained research assistant, who will obtain informed consent prior to baseline assessment and after going over the study procedures. In case the home visit cannot take place, study procedures will be explained over the phone and informed consent is obtained by mail.

Additional consent provisions for collection and use of participant data and biological specimens {26b}

On the consent form, participants will be asked if they agree to storage and use of their personal information for future research on brain injury or case management and if they agree to be approached for participation in future studies. By signing the consent form, participants give permission to the use of their data should they choose to withdraw from the study, for the research team to request injury-related information from their medical files and to share data with the regulatory authorities and the clinical research monitor of Maastricht University, where relevant. This trial does not involve collecting biological specimens for storage.

Interventions

Explanation for the choice of comparators {6b}.

Case management is compared to the usual care as this is the current alternative.

Intervention description {11a}

• Case management

The framework

The framework for case management for ABI was developed based on a combination of the taxonomy for case management [ 24 ] and descriptions of case management for dementia in the Netherlands (e.g. [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ]), because this form of case management is reasonably well integrated in the Dutch health care system. Such services can serve as a base for care innovations for people with ABI because of the shared focus on supporting people to adapt to the consequences of a disorder or disease in daily life [ 47 ].

Case management aims to support people with ABI and family member’s’ self-management of the consequences of ABI and psychosocial well-being, to prevent (escalation of) problems and to facilitate timely access to appropriate services. We propose the following case management elements:

Monitoring: tracking functioning and well-being of people with ABI and family members. In the present study, a digital monitoring system is used for this purpose (described below).

Identification: identification of questions, problems and needs (based on monitoring) that hinder functioning and well-being at the time they emerge.

Assessment: assessing the nature and severity of the presented problem, burden on and capabilities of the person with ABI and the family member, the role of their social network, making implicit or unmentioned questions and problems explicit, drawing conclusions about the core problem in the individual context.

Information (psycho-education): providing information and education on the (impact of) ABI to assist understanding, information or education related to the question or problem (with a focus on capabilities to self-manage the problems), informing on available care and support services.

Provision of support: guiding decision-making with regard to managing the problem, providing practical or psychosocial support for relatively mild problems (focused on maintaining or improving self-management).

Referral: referring to more specialized care or support for relatively complex problems and guiding decision-making with regard to what available services to use.

Coordination: supporting access to services, facilitating collaboration between different service providers and bringing about appropriate care when this is not available through the regular services.

Case management is person-centred and supposed to follow the ‘stepped care’ and ‘matched care’ principles, starting with the least complex form of care and support that meets the demand for help (stepped care), with the form and intensity individually built around the needs and capabilities of the person with ABI and/or the family member (matched care). Case manager activities may therefore vary from offering a listening ear or providing information and advice, to intensively coordinating longer-lasting specialist care. Case manager involvement also may vary in intensity over time, ranging from only monitoring to more intensive involvement.

Case management activities are community-based; they will take place at peoples’ home or other relevant places such as at work, or over the phone, via video calls or via email. While following the stepped care principle, case managers are flexible in the actions (interventions) they choose based on their own professional expertise, the links they make with available services (e.g. they are independent) and the way they create support when available services do not match people’s needs. Part of this study is mapping actual case manager activities onto the proposed elements and exploring whether this concept should be adjusted based on case manager and participant experiences, in order to move towards a more detailed description of what these elements entail in practice.

Monitoring tool (the ReMinder)

Participants receiving case management will be entered into a digital monitoring tool, called the ReMinder, developed by authors KHMJ and MD. This tool was originally developed to empower people who leave the hospital after a head trauma or stroke and their family to find information and get easy access to care in case of the development of problems caused by the injury on the long term. In the current research project, ReMinder is incorporated within OZO Verbindzorg, an online communication system that links different service providers through an online platform. The participant decides who gets access to the information in their OZO Verbindzorg account. For this project, this concerns sharing responses to the ReMinder questions (see below) with their case manager. Participants are in control of their account and may add other professionals involved in their care. They are in control of what information is shared with whom. All procedures are conducted according to the prevalent laws for personal data and privacy.

People with ABI and family members, each having their own account, will automatically receive an email every 3 months, which allows them to enter the monitoring system and to answer two questions: (1) Do you experience problems as a consequence of brain injury? (2) Are you (Is your loved one) able to do all the things you were (he/she was) doing prior to the brain injury? Both questions can be answered with yes or no. If the participant responds ‘yes’ to question 1 and/or ‘no’ to question 2, they are directed to a 32-item questionnaire asking about functioning in the areas of health, daily life, activities, social contacts and consequences of ABI. This questionnaire was composed by the developer of the ReMinder (authors KHMJ and MD), inspired by the Checklist for Cognitive and Emotional Consequence of Stroke (CLCE-24 [ 48 ]), the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P [ 49 ];), Outcome Questionnaire (OQ-45 [ 50 ];) and the life domains of the ICF: International Classification of Functioning, Disability and Health (ICF-model). Participants fill out the questionnaires as self-report. Family members fill out the questionnaire reporting about the person with ABI and five extra questions about his or her own well-being. In order to facilitate easy access to the case manager, at completion of the 32-item questionnaire participants will be asked if they would like to have contact with the case manager (yes/no). In addition, people with ABI and family members also have the opportunity to ask a question to the case manager directly at any time within the online environment.

The responses are visible to the case manager to keep track of functioning and well-being of the person with ABI. The case manager will contact those participants who explicitly indicate that they would like to get in touch. For participants who do not initiate contact with their case manager themselves, the case manager determines whether to contact the participant after the second time participants have filled out the ReMinder (i.e. 3 months after the first ReMinder questionnaire). The decision to get in touch with the participant will generally be based on whether responses indicate that certain problems have not been resolved (consistently low scores in one or more areas), a deterioration in scores, or profound discrepancies in scores between the person with ABI and their family member.

The case manager

The case manager is a fixed contact person; building a relationship is desirable because of the complexity of learning to live with ABI within the individual context, and to prevent the person with ABI and their family from having to tell their story over and over again. A back-up case manager will be assigned as well, who will be involved when the primary case manager is unavailable and who can serve as a sparring partner for the primary case manager.

The case managers form three teams, one in each region. Teams are composed of professionals from different disciplines and with varying backgrounds; some have been working in clinical/rehabilitation settings, others in community outreach, and their experience with support and treatment approaches may be focused on cure or on care. Two of the teams include a peer support worker.

Case managers will participate in a 4-day training prior to the start of the study. The training has a coaching character, promoting team members to draw upon each other’s knowledge and experience. During the training, case managers learn to see beyond one’s own specific professional discipline, get to know the professional background of the other team members and are coached in learning from each other to best support people with ABI and their family. Regular supervision meetings are organized with case managers within regions at least every 2 months, and between regions twice a year.

Care as usual

The usual care differs depending on the regional structures and collaborations. In all regions, limited structured care is available for people who suffered a stroke, mostly for secondary prevention purposes, with a limited duration of 1 year. No structural care is provided for other types of ABI. People with ABI can make use of different forms of care that may or may not involve professionals with expertise on ABI, such as physiotherapy, occupational therapy or social work, but patients usually need to take initiative to find and access these services either themselves or through their general practitioner.

Criteria for discontinuing or modifying allocated interventions {11b}

By design, case management is a modifiable form of care, as it is about organizing interventions based on the individuals’ needs, strengths, weaknesses and living situation. As described before, this study is of a pragmatic nature, granting case managers the flexibility to act as they see fit to support participants, while providing the least amount of support necessary, to stimulate self-management.

Complete discontinuation of case management will occur on participants’ request. People with brain injury can continue if their family member wishes to stop. Off note, a form of ‘active discontinuation’ occurs when participants do not need any support at a given moment: case managers will then monitor the participants’ well-being via the ReMinder and will be available when questions or problems do occur, but do not reach out otherwise. Following the intention-to-treat principle, participants who choose to withdraw from case management (i.e. the intervention) will still be asked to participated in the study assessments. Outcomes are no longer collected when participants choose to withdraw from the study (i.e. the assessments).

Strategies to improve adherence to interventions {11c}

The ReMinder serves as the basis for case managers to monitor if support is needed; participants will receive reminder emails twice a week until they open the ReMinder questionnaire. When two consecutive ReMinder questionnaires (i.e. 3 months apart) are not filled out, participants will be asked by email whether they have trouble getting access to the system, with filling out the questions or whether there is another reason for not using the ReMinder. If they do not respond to any of these emails, their case manager will reach out to explore the reason for this and to determine if further case manager activities are required. Other elements of case management are not subject to adherence as such.

Relevant concomitant care permitted or prohibited during the trial {11d}

All participants are allowed to receive any form of care that they need. Service use will be measured with a questionnaire. Participants are asked not to participate in any other studies concerning psychosocial or pharmacological care for the duration of this trial.

Provisions for post-trial care {30}

Depending on the results of the study and available funding, case management will be continued or gradually scaled down. In case of continuation, participants in the control condition will have the opportunity to receive case management after the study period.

Outcomes {12}

Effectiveness.

Outcomes measures were chosen according to the aim of case management, which involves the concepts of psychosocial well-being, self-efficacy and (unmet) needs. Assessment will take place at baseline, after 6, 12, 18 and 24 months. The total score of the Hospital Anxiety and Depression Scale (HADS [ 51 ];) will serve as the primary outcome measure, other measures (see below) are secondary outcomes.

Outcomes for people with ABI

Psychosocial well-being will be assessed using the HADS, the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P [ 49 ]) restriction subscale and the Life Satisfaction Questionnaire (LiSat [ 52 ]). The HADS consists of 14 items scored on a 4-point scale ranging from 0 to 3 with varying anchors. Total scores can be calculated for the full scale (primary outcome). The two subscales (anxiety and depression) will also be computed and analysed as secondary outcomes; subscale scores of > 7 suggest the presence of an anxiety disorder or depression. The psychometric quality of the scale is sufficient [ 53 ]. The USER-P restriction subscale consists of 9 items asking about restrictions in vocational, leisure and social activities as a consequence of ABI. Items are rated on a scale from 0 (not possible) to 3 (without difficulty) and a ‘not applicable’ option. The total score ranges from 0 to 100 based on the number of applicable items; higher scores indicate less restrictions in participation. The scale has shown sufficient reliability and validity [ 49 ]. The LiSat assesses various aspects of life satisfaction including life as a whole, self-care management, contacts with friends, vocational, family life, partner relationships, financial situation, leisure situations and sex life. The nine items are scored on a 6-point scale ranging from ‘very dissatisfied’ to ‘very satisfied’. The scale has satisfactory reliability and validity [ 54 , 55 ].

The concept of self-efficacy, one’s confidence in the ability to deal with (health) problems, will be measured as a proxy for self-management, since self-efficacy is a prerequisite for behavioural change. Self-efficacy will be measured with the Patient Activation Measure (PAM [ 56 ];), which is a 13-item instrument assessing self-reported knowledge, skills and confidence for self-management of one’s health or chronic condition. Items are scored on a scale of 0 (disagree strongly) to 4 (agree strongly) and a not applicable option. An algorithm is available to transform the scores on the PAM to different levels of self-management, from ‘disengaged and overwhelmed’ to being their own health advocate. The PAM requires a license and sharing of the de-identified data with Insignia Health. The Dutch version of the PAM has shown moderate test-retest ability ( r  = 0.47) [ 57 ].

Care needs will be assessed with the Longer-term Unmet Needs after Stroke questionnaire (LUNS [ 58 ];). The LUNS consists of 22 items scored with ‘yes’ or ‘no’ and one open ended question on the physical, social, and emotional consequences of stroke. To make the LUNS applicable for people with other types of brain injury, we replaced the word ‘stroke’ by ‘brain injury’ in two items. A validation study of the Dutch version of the LUNS concluded that the scale is reliable and valid [ 59 ]. It should be noted that some items merely express worries or a problem rather than needs (e.g., ‘I am worried that I might fall [again] and this is stopping me from doing my usual things’). Nevertheless, we consider the scale to be the most comprehensive scale to assess care needs in the ABI population available in Dutch.

Family member outcomes

Psychosocial well-being, self-efficacy and (unmet) needs will also be measured in family members. In addition to the HADS and LiSAT, of which a description is provided above, caregiver burden will be assessed within the concept of psychosocial well-being. The Caregiver Strain Index (CSI [ 60 ];) will be used, which consists of 13 items that can be responded to with ‘yes’ or ‘no’ and total scores ranging from 0 to 13; higher scores reflecting higher caregiver burden and substantial burden is indicated by a score of 7 or higher. For people who suffered from stroke, the CSI is the most commonly used scale and recommended in the Dutch stroke care guidelines [ 61 ]. The scale has shown sufficient validity and reliability [ 62 ].

S elf-efficacy will be assessed using the Carer Self-Efficacy Scale (CSES) [ 63 ]. The CSES measures self-efficacy with regard to care management and service use, each in 5 items on a 10-point scale from ‘not at all certain’ to ‘very certain’; higher scores on the CSES indicate higher levels of self-efficacy. Reliability and validity of the scale are sufficient [ 64 ].

Family members’ needs will be assessed with the Family Needs Questionnaire (FNQ [ 65 ]). The scale includes 40 items assessing needs that may arise during acute rehabilitation, soon after discharge and in the long-term after ABI. Subscales include health information, emotional support, instrumental support, professional support, community support network and involvement with care. Family members are asked to indicate the importance of each perceived need and then rate the degree to which the need has been met. The Dutch translation has shown sufficient reliability [ 66 ]. Information on the validity of the translation is not yet available, but the English version has shown to be valid [ 67 ].

Cost-effectiveness and cost-utility

Cost-effectiveness and cost-utility will be determined using the EuroQol (EQ-5D-3L) and a service use questionnaire, included in the assessments on baseline and after 6, 12, 18 and 24 months. The EQ-5D-3L [ 68 ] consists of five questions measuring health status. The dimensions covered are mobility, self-care, daily activities, pain or discomfort, and anxiety or depression. These domains are rated as ‘no problem’, ‘moderate problem’ or ‘unable to do’. The EQ-5D-3L has shown good measurement properties [ 69 ]. Service use will be measured with a self-report cost questionnaire, which was constructed to collect cost data from a societal perspective. It is based on the steps described by Thorn and colleagues [ 70 ] and on the questionnaire used by Rauwenhoff and colleagues [ 71 ].

Feasibility

The assessment of feasibility is of exploratory nature and will be assessed using the process evaluation framework of Saunders, Evans and Joshi [ 72 ]. This involves mapping fidelity (quality), dose delivered (completeness), dose received (exposure), reach (participation rate), recruitment (procedures, maintenance of participant involvement) and context (aspects of the physical, social, and political environment). Data will be collected continuously in the form of registrations by case managers, and focus groups will be held at study end (18–24 months after baseline). Data will primarily be used in a summative and descriptive manner. Quantitative indicators to determine feasibility are:

At least 67% of the participants fills out the ReMinder each wave (every 3 months).

Case managers respond to at least 90% of the times patients the request for contact and contact patients 90% of the times this is indicated by the responses in the ReMinder.

Satisfaction with case management (beyond monitoring) is rated with a 7 or higher on a scale of 1–10.

At least 70% making use of case management (beyond monitoring) would recommend case management to others.

Qualitative indicators are:

Participants and case managers reporting on case management in terms of it being acceptable, feasible and useful.

Participants reporting on case manager activities to match their needs (matched care).

Participants and case managers reporting on increasing support when necessary and taking steps back when possible (stepped care)

Participants reporting on case managers’ expertise and skills, case managers reporting on feeling well-equipped to appropriately support participants’ needs.

Other study parameters

The following demographic and injury-related characteristics will be collected at baseline: date of birth, gender, education, date of most recent ABI, type of most recent ABI, date and type of previous ABI(s), hospital admission (yes/no), length of hospital stay of most recent ABI, referral destination at hospital discharge (home or rehabilitation centre).

Participant timeline {13}

The schedule of enrolment, interventions and assessments can be found in Table  1 . All participants will receive the study questionnaires every 6 months until December 2021. Depending on the time of inclusion (up to June 2020), people with ABI will be followed up for 18 to 24 months. A subsample of people with ABI and family members will be approached for additional participation in focus group interviews, taking place at the end of the study (between October and December 2021).The evaluation form will be sent after 1 year and at the final measurement, which can be 18 or 24 months after baseline assessment depending of the time of enrolment.

Sample size {14}

Power calculation was based on the primary outcome measure Hospital Anxiety and Depression Scale (HADS). A study evaluating a monitoring/psycho-educational intervention in patients with possible ABI due to cardiac arrest showed to be effective in improving both anxiety and depressive symptoms with a group difference on the HADS of 3.25 points, corresponding to Cohen’s d effect size of 0.36 [ 73 ]. With an alpha of .05 and power value of 80%, a sample size of 194 is required to detect such between-group effect in the post-intervention measurement. This number can be adjusted for the correlation between baseline and follow-up data, since baseline measures will be entered in the model as an independent variable, by multiplying the sample size by 1 −  R 2 (R is the population correlation between the dependent variable (post-intervention) and the pre-intervention measurement) [ 74 ]. R is estimated to be at least 0.5, making the sample size 194 × (1 − 0.5 2 ) = 146. Taking a drop-out rate of 30% into account, at least 209 people with ABI should be recruited. For each participating person with ABI, the family member who is or would be acting as the informal caregiver will be asked to be enrolled in the study as well. The number of participating family members will not exceed the maximum of 209.

Recruitment {15}

At each of the three recruiting hospitals, trained hospital staff will select eligible people with ABI from the electronic patient files. The hospital staff explains the aim of the study and the study procedures to the patient and ask whether they have a family member who might be interested in participating as well. When the person with ABI or person with ABI-family-member couple is interested in participation, the hospital staff will send them the study information and notifies the researcher. The researcher will call after a week to clarify any questions patients or family members may have. If they are interested in participating, an appointment will be scheduled with the research assistant to sign informed consent, complete the baseline assessment and perform the randomization. In case the person with ABI is referred to inpatient rehabilitation, the appointment will take place as soon as possible after discharge.

Assignment of interventions: allocation

Sequence generation {16a}.

Participants (people with ABI or people with ABI-family-member couples) will be randomly allocated with a 1:1 ratio to either the case management group or the care as usual group, using a computerized random schedule, in blocks of six. The randomization block size will not be disclosed to the research assistant who enrols and assess participants, to ensure concealment.

Concealment mechanism {16b}

The research assistant will be provided with sequentially numbered, opaque sealed envelopes containing randomization information.

Implementation {16c}

The allocation sequence will be generated by a person who is not involved in the study assessments, using a computerized random number generator ( www.random.org/lists/ ). This person will prepare sequentially numbered opaque envelopes containing the information about the group assignment of the participant and seal and the envelopes. The research assistant, who is blind for the allocation sequence and block size, will enrol participants, open the envelope after baseline assessment is completed and provide participants with the information about treatment allocation.

Assignment of interventions: blinding

Who will be blinded {17a}.

At baseline assessment, group allocation will be unknown to both the research assistant and the participants; treatment allocation takes place after completion of the baseline assessment by a research assistant. If people with ABI wish to be visited at home for the follow-up assessments rather than receiving the questionnaires via mail, they will be visited by a member of the research team who is blind to their treatment allocation. Blinding is not possible for the assessment of feasibility.

Procedure for unblinding if needed {17b}

There are no foreseen circumstances under which unblinding is necessary.

Data collection and management

Plans for assessment and collection of outcomes {18a}.

Several methods will be used to obtain information on the process evaluation outcomes: (1) registration forms for case manager activities, (2) written notes of supervision meetings, (3) responses to the questionnaires and participants’ communication with case managers derived from the ReMinder and the OZO system, (4) evaluation forms after 12 months and at study end for participants (both people with ABI and family members) receiving case management and (5) focus group interviews at study end.

Focus groups

Focus groups will be held to obtain in-depth information on the experiences with receiving case management (for participants) and with delivering case management (for case managers). Focus groups are group interviews (approximately 6 participants each) with a particular subject (or focus), and they make use of social interactions between participants. Focus groups are an ideal method to reveal various perspectives on a topic and to uncover new insights and unanticipated issues [ 75 ]. During the focus groups, a moderator (researcher) will use a discussion guide that will include questions on the topics based on fidelity, dose delivered, dose received, reach, recruitment and context [ 72 ]. The interviews will be audio recorded as well as video recorded, to ensure identification of potentially relevant non-verbal information or cues presented by the participants. A second researcher will take additional notes during the focus group interviews.

People with ABI and family members of the three different regions will be selected to form a sample including a wide range of injury-related characteristics and needs (purposively selected) to participate in focus group interviews at the end of the study period. In each region, one people with ABI focus group and one family member focus group will be planned (six in total). This should be sufficient to achieve saturation, i.e. when no new issues emerge from the last focus group, as research suggests that saturation is usually achieved after the fourth group discussion [ 75 ]. Saturation will be checked after these groups and if necessary, additional focus groups will be planned. Because the case managers form a limited group within the scope of the project (25–30 case managers), we aim to include all case managers in focus groups, divided in 3–4 groups.

Effectiveness and cost-effectiveness

The baseline assessment will be completed no more than 2 months after the visit to or discharge from the hospital or, in case of inpatient rehabilitation following hospital discharge, as soon as possible after they leave the rehabilitation centre. Ideally, assessments take place at the participants’ home, but can take place via telephone or questionnaires may be sent and returned by mail in case home visits are not possible (on time). A trained research assistant will explain the study procedures once more and collect the consent form. During home visits, participants fill out the questionnaires on their own and have the opportunity to ask questions. The research assistant will provide support when necessary, for example by reading the questions and possible answers out loud in case of reading difficulties (a common problem after stroke). Information on gender, educational level and living situation will be collected at baseline; date of birth, type and date of most recent and previous brain injuries and discharge destination will be drawn from hospital records by hospital staff. Follow-up measurements will be mailed to the participants, unless people prefer to be assisted, in which case a research assistant (blind to treatment allocation) will visit them at home.

Plans to promote participant retention and complete follow-up {18b}

Participants will receive a five-euro gift card for each measurement (maximum of 25 euros for the complete study). For participating in the focus groups, they will receive an additional fee of ten euros (gift card).

Data management {19}

Data will be collected on paper and then entered electronically twice by members of the research team; any discrepancies between the two entries will be resolved by the person responsible for the second entry, or by discussion with a third researcher if necessary. There are restrictions in place for entering questionnaire data based on the range of answers. Manual range checks will be performed for demographic information. An audit trail will provide with information on all activities in the electronic database. Access to electronic data is controlled by a password system, and access to original data will be restricted by storing the data in a locked cabinet (see ‘ Confidentiality {27}’).

Confidentiality {27}

Data will be handled confidentially and reporting will be coded. All participants will receive a unique identifier that cannot be used to link the data to an individual subject (i.e. CM001). Collected data and personal information will be stored separately in locked cabinets. The involved researchers from Maastricht University will safeguard the key to the code. Only the national supervisory authorities such as the Inspection for Healthcare and Youth (in Dutch: Inspectie voor Gezondheidszorg en Jeugd) will have access to the data upon request. The handling of personal data will comply with the EU General Data Protection Regulation (GDPR) (in Dutch: AVG), the Dutch Act on Implementation of the General Data Protection Regulation, and the Research Data Management Code of Conduct of Maastricht University. The data will be stored for 15 years after the end of the study.

Plans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use {33}

There will be no collection of biological specimens.

Statistical methods

Statistical methods for primary and secondary outcomes {20a}.

Multilevel modelling will be used to assess the improvement of case management participants over time compared to care as usual. Primary analysis involves entering time, group and their interaction (exposure) as fixed terms. Separate analyses will be performed to assess 24-month outcomes for the subgroup of participants for whom this data is available. Sensitivity analysis will be performed by extending the resulting models with covariates, controlling for the effects of age, gender and level of education. Covariates will be kept in the model as long as they contribute significantly to the model. Significance of the fixed regression effects will be tested using the appropriate t -test ( α  = .05).

The trial-based economic evaluation will involve a combination of a cost-effectiveness analysis (CEA) and a cost-utility analysis (CUA). Effects will be presented as clinical outcomes (i.e. self-efficacy and psychosocial well-being). In these CEAs, the incremental cost-effectiveness ratio (ICER) will be expressed as the incremental costs per point improvement on the primary outcome measure. The primary outcomes measure for the CUA will be quality-adjusted life years (QALYs), based on the EuroQol (EQ-5D-3L). The EQ-5D-3L can distinguish between different health states. For each of the different states, a weight is contributed based on the valuation given by the general population (Euroqol group). These range from 0 (representing death) to 1 (representing full health). Cost-effectiveness evaluations make use of these utilities. In the CUA, the ICER will be expressed as the incremental costs per QALY. This economic evaluation will be performed from a societal perspective, which implies that all relevant costs and outcomes will be considered. The time horizon will be the same period as the follow-up period of the trial.

Total costs will be estimated using a bottom-up (or micro-costing) approach, where information on each element of service used is multiplied by an appropriate unit cost and summed to provide an overall total cost. The economic evaluation will assess not only the intervention costs, but also healthcare costs, patient and family costs, and costs outside the health care sector. For this study, we have developed a cost questionnaire, based on existing questionnaires which will identify all relevant costs aspects.

Despite the usual skewness in the distribution of costs, the arithmetic means will be generally considered the most appropriate measures to describe cost data. In case of skewness of the cost data, non-parametric bootstrapping will be used to test for statistical differences in costs between the case management and care as usual group. The bootstrap replications will be used to calculate 95% confidence intervals (CI) around the costs (95% CI). If cost data are distributed normally, t -tests will be used. The robustness of the ICER will be checked by non-parametric bootstrapping (1000 times). Bootstrap simulations will also be conducted in order to quantify the uncertainty around the ICER, yielding information about the joint distribution of cost and effect differences. The bootstrapped cost-effectiveness ratios will be subsequently plotted in a cost-effectiveness plane, in which the vertical line reflects the difference in costs and the horizontal line reflects the difference in effectiveness. The choice of treatment depends on the maximum amount of money that society is prepared to pay for a gain in effectiveness, which is called the ceiling ratio. Therefore, the bootstrapped ICERs will also be depicted in a cost-effectiveness acceptability curve showing the probability that case management is cost-effective using a range of ceiling ratios.

Registration forms, evaluation forms and written notes of supervision meetings will be analysed descriptively, and focus groups will be analysed qualitatively. Audio and videotapes of all focus group interviews will be transcribed verbatim. Analyst triangulation will be applied [ 76 ]; the transcripts and observations combined with additional notes that will be taken during the focus group interviews will be analysed independently by two researchers using the qualitative analysis software ATLAS.ti (version 7.0). An inductive content analysis approach will be adopted [ 77 ], in which common themes and categories emerge using inductive reasoning and constant comparison. The texts will be thoroughly read and open codes will be applied to describe all aspects of the content [ 78 ]. Codes referring to the same phenomenon will be grouped into categories and these categories will be grouped into higher-order themes. Categories and themes will be combined into general statements to describe the phenomenon [ 77 ]. Discrepancies in coding and interpretation will be discussed in a meeting together with a third researcher to reach consensus regarding the categories and themes. The video recordings will be compared with the written transcripts to be able to identify potentially relevant additional non-verbal information or cues presented by the participants. Quotations will be selected based on representativeness of the emerged themes by the coordinating researcher and verified by the other two researchers.

Demographic and injury-related characteristics will be analysed descriptively.

Interim analyses {21b}

No interim analyses are planned because there are no anticipated risks to participation in this study.

Methods for additional analyses (e.g. subgroup analyses) {20b}

Subgroups will be formed based on type of injury (stroke vs. other types) and severity, for which length of hospital stay will be used as a proxy (based on the distribution of the sample). Even though the subgroups are likely to be too small to draw firm conclusions, we will analyse this exploratively because the usual care for stroke is already better organized than for other types of brain injury (possibly lowering the additional gains of case management over care as usual for stroke compared to other types of brain injury) and because those with moderate to severe ABI may have more to gain from case management than those with mild ABI.

Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c}

Data will be analysed by the intention-to-treat approach. Sensitivity analysis will be performed with regard to missing data; we will evaluate which of the measured variables are associated with missing outcomes and will include these in the model. Reasons for drop-out or missing assessments will be documented if participants are willing to share this.

Plans to give access to the full protocol, participant level-data and statistical code {31c}

The full protocol, anonymized data set and statistical code will be available on request after the results of the study have been published.

Oversight and monitoring

Composition of the coordinating centre and trial steering committee {5d}, principal (cmvh), coordinating investigators (apms; cr interim).

Design of the study;

Preparation of protocol and revisions;

Ethics committee application;

Study planning;

Recruiting, training and supervising research assistants;

Responsible for trial master file;

Provide annual reports to ethics committee;

Data verification;

Publication of study reports;

Project team

Principal (CvH) and coordinating (APMS; CR interim) investigators, overall project leader (JZ), intervention project leader (NJ), ReMinder monitoring tool project leader (KJ, MD)

Agreement of final protocol;

Recruiting hospital staff and assistance with recruiting procedures;

Implementing ReMinder;

Entering participants in case management group into monitoring tool;

Recruitment, training and supervising case managers;

Collecting registrations of case management activities;

Organizing project team meetings.

Composition of the data monitoring committee, its role and reporting structure {21a}

Because of the low burden and minimal risks, no data monitoring committee was appointed.

Adverse event reporting and harms {22}

We will only report those adverse events that are directly related to our study, defined as experiencing negative consequences of case management in terms of psychosocial well-being or self-management that are reported spontaneously by the subject. Due to the non-invasive nature of this study, no experiment-related (serious) adverse events are expected.

Frequency and plans for auditing trial conduct {23}

As this study falls under the scope of the Dutch Medical Research Involving Human Subjects Act (Dutch: wet medisch-wetenschappelijk onderzoek met mensen, WMO), the Clinical Trial Centre Maastricht (Maastricht University) appointed an independent clinical research monitor to the study. This person monitors whether the study is conducted according to the ICH-GCP guidelines and legislation and regulations. For our study, four visits divided over the study period are planned.

Activities of the clinical trial monitor are:

Giving advice regarding laws and regulations;

General control of data collection;

Verification of source documents and CRFs;

Controlling the compliance of laws and regulations;

Complying all protocols;

Checking of informed consents;

Controlling the Trial Master File;

Verifying the reports on adverse events and complications.

Plans for communicating important protocol amendments to relevant parties (e.g. trial participants, ethical committees) {25}

All changes (substantial and non-substantial) made to the study protocol after the favourable opinion was given by the accredited medical ethics committee (Medical Ethics Committee of Maastricht University Medical Center+) will be notified to the medical ethics committee, documented in the trial registration and communicated in the publication of the results of this study.

Dissemination plans {31a}

The results, whether positive or negative, will be disclosed unreservedly and submitted for publication to peer-reviewed scientific journals and presented on national and international conferences and meetings for healthcare professionals and people with ABI.

It is essential that people with ABI are supported in learning how to live with ABI, within the individual context in terms of home, education, work, relationships, stage of life and personal goals [ 14 ]. Creating a continuous chain of support from hospital discharge onwards is high on the agenda of guidelines and action plans for different types of ABI [ 12 , 13 , 14 , 15 ]. Continuous and long-term support is currently lacking, as is evident from numerous studies reporting this an important unmet need for people with ABI and caregivers [ 9 , 10 , 11 ] and the lack of randomized controlled trials on longer-term care. We respond to this issue by developing and evaluating case management for people with ABI and their family members. We will evaluate whether case management for ABI is feasible, effective and cost-effective compared to care as usual with a randomized controlled trial.

Strengths of the study are the pragmatic nature of the study and the long-term follow-up. The follow-up with a maximum of 24 months approximates the time it usually takes for people to regain a balance in their lives [ 10 ]. Furthermore, the pragmatic nature allows us to evaluate case management the way it could be implemented in regular practice. The use of the monitoring tool (the ReMinder) is another strength, as it takes a minimum amount of time and effort to keep track of a large group of people for long periods of time. Finally, the combination of qualitative and quantitative evaluation methods should provide us with rich data on feasibility, effectiveness and cost-effectiveness.

A possible limitation of our study is that for a complete picture of cost-effectiveness and cost-utility of case management, the limits placed on the time frame of our study (18–24 months) will be sufficient to capture a long-term reduction in costs. That is, we expect costs to rise in the first year(s) as case management aims to support in getting timely access to services, while the expected longer-term reduction in costly intensive support may extend our study period. Another possible limitation is that we did not define inclusion criteria with regard to severity of ABI, which means that we will include people with mild ABI who may recover well without support; if this group turns out to be large, they may end up masking effects for the more severe group who benefits from case management. Nevertheless, we deliberately chose to include this group, because care continuity for people experiencing problems after mild ABI is currently missing. Case management could fill this gap; by monitoring people with mild ABI, those with suboptimal recovery can be identified and supported, while those who do fully recover require little case manager time, effort and costs.

By evaluating case management for ABI, this study aims to move forward in bridging the gap between the available care and the needs of people with ABI and their family members. If our study shows promise for case management to be (cost)-effective and feasible, it could be a valuable form of regular care to support people with ABI and their family members in finding a new balance in life.

Trial status

The Medical Ethics Committee of Maastricht University Medical Center+ granted ethics approval of the third version of the protocol on September 17, 2019. The trial was registered at the Netherlands Trial Register (registration number NL8104, https://www.trialregister.nl/trial/8104 ) on October 22, 2019, after which recruitment started. The first person was enrolled on November 25, 2019. Inclusion is currently ongoing and expected to be completed in September 2020.

Abbreviations

Cost-effectiveness analysis

Confidence interval

Carer Self-Efficacy Scale

Caregiver Strain Index

Cost-utility analysis

Family Needs Questionnaire

General Data Protection Regulation

Hospital Anxiety and Depression Scale

Incremental cost-effectiveness ratio

Life Satisfaction Questionnaire

Longer-term Unmet Needs after Stroke

Patient Activation Measure

Quality-adjusted life years

Utrecht Scale for Evaluation of Rehabilitation-Participation

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Authors’ contributions {31b}

APMS and CMvH developed the study protocol. CR, MD, NJ, KHMJ and JMZ contributed to the study design and procedures. APMS wrote the first draft of this manuscript. All authors have read and approved the final version of this manuscript.

Competing interests {28}

The authors declare that they have no competing interests.

Funding {4}

This study is supported by a grant from the Dutch Ministry of Health, Welfare and Sport to Stichting InTussen. Both the funder and the sponsor (Maastricht University) had no role in the study design and will have no role in the collection, management, analysis and interpretation of data nor in the decision to submit the report for publication.

Availability of data and materials {29}

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request after publication of the results.

Consent for publication {32}

Not applicable.

Ethics approval and consent to participate {24}

The Medical Ethics Committee of Maastricht University Medical Center+ approved the trial (registration number METC19-040). The study will be conducted according to the principles of the Declaration of Helsinki (World Medical Association, October 2013) and in accordance with the Dutch Medical Research Involving Human Subjects Act (Dutch: WMO). All people with ABI and family members will provide informed consent prior to participation.

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Annemarie P. M. Stiekema & Caroline M. van Heugten

Limburg Brain Injury Center, Maastricht University, Maastricht, The Netherlands

Annemarie P. M. Stiekema, Christine Resch & Caroline M. van Heugten

Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands

Christine Resch & Caroline M. van Heugten

Health Care and Social Work Division, Windesheim University of Applied Sciences, Almere, The Netherlands

Mireille Donkervoort & Kitty H. M. Jurrius

Mevrouw Slimmer Werken Social Innovation in Health Care and Well-Being, Drogteropslagen, Netherlands

Natska Jansen

Brain Injury Team, Overijssel, Netherlands

In-Tussen Foundation, Utrecht, the Netherlands

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Stiekema, A.P.M., Resch, C., Donkervoort, M. et al. Case management after acquired brain injury compared to care as usual: study protocol for a 2-year pragmatic randomized controlled superiority trial with two parallel groups. Trials 21 , 928 (2020). https://doi.org/10.1186/s13063-020-04804-2

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Post-Traumatic Stress Disorder

New study shows brain change after psychological trauma, research on important findings in earthquake survivors with and without ptsd..

Posted December 5, 2018 | Reviewed by Davia Sills

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"The drama and the trauma of the relationship you have when you're 16 can mirror the one you have when you're 26. Life repeats itself." —Taylor Swift

Post- traumatic stress disorder ( PTSD ) is a relatively common condition, affecting nearly 7 percent of people over the course of a lifetime, and over 3.5 percent of people in any given year, according to the NIMH from National Comorbidity Survey data.

Other estimates suggest even higher rates, more so in at-risk groups. PTSD is more than two times higher in women than men, and it is associated with higher suicide rates.

Fifty to 70 percent of U.S. citizens are expected to experience major trauma in a lifetime, and the estimated costs resulting from trauma amount to over $40 billion a year. The burden of PTSD is great in terms of personal suffering, and the impact on family, community, and society from psychosocial and economic perspectives.

What is PTSD?

PTSD is characterized by exposure to a traumatic event, or traumatic events, and may come on shortly after trauma or at a future time. PTSD may be relatively short-lived, or it may be long-lasting, becoming chronic.

PTSD symptoms include a blend of re-living and re-experiencing the trauma—for example via intrusive thoughts, nightmares, or repetitive behaviors ( even repeating traumatic relationship patterns )—negative changes in emotions and thinking, for example depressed mood and difficulty with clarity of thought or memory ; dissociative symptoms, such as detachment or emotional numbing; avoidance of reminders and thoughts of trauma, which may severely limit one’s choices or keep one from leaving the home; and hyperarousal symptoms, like anxiety , being constantly on edge, or being fearful, rageful, and generally on high alert at all times.

PTSD can only be diagnosed if more than four weeks have passed since a traumatic event; prior to that point, most reactions are considered normal, but if severe may verge diagnostically into Acute Stress Disorder. PTSD, early on in life especially, can lead to complex PTSD (cPTSD), with effects on the development of personality and identity , generally resulting from abuse and neglect as a child .

PTSD and the stress-response system

In general, PTSD represents an exaggerated and persistent reaction to fear in which the two branches of the autonomic nervous system , which regulates the body’s balance between activation and resting, are off-kilter. Normally, the activating branch, the sympathetic nervous system, jumps into action when there is a threat, diverting resources to fight-flight systems... and then things go back to a poised, calmly ready state.

For example, blood flow to the muscles increases, the heart works harder, we become mentally far more alert and sometimes flooded with fear, and we are ready to do what is necessary to survive. When the crisis passes, the parasympathetic system kicks in, and blood flow resumes a normal pattern, the body relaxes, and we may need to use the restroom. Our emotions and thinking settle back down, and we may even at that point need to fuel up before “crashing.”

In PTSD, the activation in a basic sense appears to persist, almost like a computer glitch, and the sympathetic system gets stuck in higher gear, crudely similar to a car running at high RPMs for too long. When this happens, these immediate action systems adapt to chronic usage, leading to ongoing burned-out stress, and negative effects on health and quality of life.

The parasympathetic nervous system is not able to properly cool things down, and either is offline when needed—and we may turn to chemical means to settle ourselves down—or it kicks in when we don’t want it to, causing emotional detachment, fatigue, and a variety of symptoms related to imbalances throughout our systems.

We are not talking about healthy grief or growth after trauma here, though that is an important part of the story and one I personally treasure, but rather prolonged suffering, which serves no additional purpose. It's fine to make lemons out of lemonade if all there is are lemons, but not when there is a fruit stand right next door which we can't seem to get to no matter how hard we try.

It's truly wonderful to glean wisdom from adversity, but it doesn't make sense to seek out adversity in order to become wise.

The brain on PTSD

Given how diverse PTSD is, as is the case with all disorders involving the brain, we don’t have a detailed understanding of what actually happens to cause different kinds of PTSD at different time points. To date, most research has looked at a mixed group of patients with mostly chronic PTSD, with a variety of changes throughout the brain.

The areas of the brain identified to be different in PTSD include the hippocampus, which deals with our memory and sense of self ; the amygdala, a main emotion center of the brain emotional systems (limbic system) and a key player in the brain’s state at rest; and the anterior cingulate cortex, which is a key hub in networks for emotion and thought, learning, conflict-resolution, and, notably, top-down control of the limbic system via direct connections with the amygdala.

These brain areas are involved in altered network activity for people who suffer from PTSD, with changes seen in the brain’s resting “ default mode network ” and the difference in what people with PTSD look for in the environment , in the “salience network.”

Various studies of chronic PTSD have shown conflicting brain anatomical, functional, and metabolic findings in some of the same brain areas, for reasons which are not yet understood.

PTSD, psychoradiology, and earthquakes

Given the few studies of early PTSD and the importance of understanding just what is happening to the brain after a traumatic event, researchers from China and England measured the brain activity of survivors of the 2008 Sichuan earthquake with and without PTSD within seven to 15 months [N.B. of personal significance for me, as I trained mental health responders in China via teleconference right after that earthquake].

After excluding those with other disorders, medication use, prior trauma, head injury , and additional factors which would reduce the statistical power of the study, they had 78 subjects with PTSD and 71 without who were well matched.

Researchers used “proton magnetic resonance spectroscopy” (H-MRS) to gauge not only the volume of the amygdala (amygdalae, actually, or amygdalas, because there is one on each side), but also metabolic activity in the anterior cingulate cortex (cortices, or cortexes) using spectroscopy, which allows us to infer what molecules are present from analyzing the distribution of different atomic weights in a given sample. Studying metabolic activity to reflect mental activity has been called “psychoradiology,” a developing field as researcher map out which markers mean what, how to measure them, and so forth.

In this study, researchers measured N-acetyl aspartate (NAA), considered to be an indicator of neuron damage or health; creatine (Cr), a general marker of brain activity; glutamate and glutamine (Glx), excitatory neurotransmitters; choline (Cho), reflecting cell density; and Myo-inositol (mI), an indicator related to glial cells, which work in tandem with neurons in supportive and perhaps more direct ways in making up the structure of the brain and allowing our brains to function.

Their findings, while preliminary, are fascinating. They found that NAA concentration in the anterior cingulate was increased in PTSD, higher earlier on and less elevated (but still above non-PTSD) as the months passed. Previous studies of chronic PTSD lean toward lower-than-average NAA, suggesting that NAA is elevated in the early response to trauma in those with PTSD, and an as-yet-unexamined process takes place over time, and NAA drops.

As NAA is higher in other psychiatric conditions and becomes decreased over time (e.g., in depression , schizophrenia, social anxiety disorder), something important is reflected in NAA activity, hypothesized by researchers to be related to increased emotional processing. Both amygdalae were decreased in size, a finding mirroring many, but not all, previous studies. Stress has been shown to reduce amygdala size, but exactly how this happens isn’t clear, especially in the short term.

Metabolic activity was different in the left versus right amygdalae, with high Cr in the left side and high mI in the right. While it is not possible to say what it means, study authors suggest that these differences may be related to the brain’s protective reactions to injury and/or because of glial cells, which are thought to respond to injury by increasing in number and activity.

It isn’t surprising that there are differences between right and left amygdalae, because while similar in function, the different sides of the brain do not have exactly the same functions, associated with differences in amygdala response to trauma and also suggested by prior research.

Where are we in understanding and using knowledge of PTSD neuroscience?

This research is noteworthy for a few reasons. First of all, a picture of PTSD brain changes over time starts to come into focus. The prime example of this is in the finding that NAA is higher right after a traumatic experience leading to PTSD and then tapers off over time, a finding consistent with low NAA levels in long-standing PTSD. The current study allows us to connect the dots between early and later PTSD.

The NAA changes are found in other disorders, pointing to an avenue for further research to understand how chronic mental illness unfolds over time. For example, while NAA level alone may not be a specific way to diagnose PTSD, because it is common in other conditions, NAA level over time may be a way to gauge the progress of care, as could measuring changes in brain volume for conditions where treatment returns the brain to a more normal state.

The other markers are important, because they may end up being helpful stepping-stones along the path to figuring out what short- and long-term trauma and stress do to the brain. In addition, though this is a stretch, the data from this and other research might be used to construct a diagnostic tool specific for PTSD, which could help to tell PTSD apart from other conditions early on in treatment and, again, to track clinical response as well as point the way to new therapies.

More broadly, H-MRS and related psychoradiological tools will continue to transform our understanding of the “mindbrain,” 1 the holistic union of biological and psychological processes which make up our psyches, envisioned early on by Sigmund Freud in his Project for a Scientific Psychology (1895).

We are gradually developing and using new methods to catch deeper and deeper glimpses of what is going on which makes us who and what we are, in sickness and in wellness. We are coming to grasp that psychological trauma physically impacts the brain immediately following an injury, and over time.

Being able to analyze metabolic activity and understand what it means is a key part of the puzzle, along with looking at changes in brain volume and activity based on blood flow. All of these approaches can help inform our understanding of altered brain networks, a crucial conceptual tool for seeing the brain as a dynamic, measurable system; as a framework for molding brain activity back to a non-PTSD state (e.g., using targeted neuromodulation, like transcranial magnetic stimulation , or TMS, and other therapeutics); and to help understand how to support and establish resilience .

It will be important to see how the approaches used in this work develop, and how they are applied to a broader group of people. Understanding the brain processes which underlie PTSD will help us to understand how trauma repeats itself, especially with early trauma, which leaves a stronger imprint on development and personality, and how to find healthier alternatives to automatic, often helpless repetition, which persists in spite of intellectual understanding.

1. As Mark Blechner, PhD calls it in his work on the neuroscience of dreaming in The Mindbrain and Dreams: An Exploration of Dreaming, Thinking, and Artistic Creation

Xiaorui S, Chunchao X, Wang W, Huaiqiang S, Qiaoyue T, Simin Z, Lingjiang L, Kemp G, Qiang Y and Qiyong G. Abnormal metabolite concentrations and amygdala volume in patients with recent-onset posttraumatic stress disorder. Journal of Affective Disorders, online 11 August 2018.

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PTSD Case Studies: Exploring Trauma Through Real-Life Experiences

Echoes of a haunting past reverberate through the mind of our case study subject, illuminating the complex landscape of Post-Traumatic Stress Disorder and its profound impact on human resilience. This intricate psychological condition, known as PTSD, affects millions of individuals worldwide, leaving an indelible mark on their lives and challenging their ability to navigate everyday experiences. The importance of understanding PTSD through real-life case studies cannot be overstated, as these narratives provide invaluable insights into the nuanced manifestations of trauma and the diverse paths to recovery.

Post-Traumatic Stress Disorder is a mental health condition that can develop after exposure to a traumatic event, such as combat, sexual assault, natural disasters, or severe accidents. According to the National Center for PTSD, approximately 7-8% of the U.S. population will experience PTSD at some point in their lives. This prevalence underscores the critical need for comprehensive research and effective treatment strategies to address the far-reaching impacts of trauma on individuals and society as a whole.

Case studies serve as a powerful tool in the exploration of PTSD, offering a window into the lived experiences of those grappling with the disorder. By examining individual narratives, mental health professionals can gain a deeper understanding of the unique challenges faced by PTSD sufferers and develop more tailored and effective treatment approaches. These studies also help to humanize the disorder, fostering empathy and awareness among the general public and healthcare providers alike.

In this article, we will delve into the compelling case of Sarah, a 32-year-old woman whose life was irrevocably altered by a traumatic event. Through Sarah’s journey, we will explore the intricate web of symptoms, diagnostic processes, and treatment modalities that characterize the landscape of PTSD. Her story serves as a testament to the resilience of the human spirit and the transformative power of proper care and support.

Background of the PTSD Case Study

Sarah, our case study subject, is a 32-year-old marketing executive from a bustling metropolitan area. Prior to her traumatic experience, Sarah led a vibrant and successful life, balancing a demanding career with an active social life and a passion for outdoor activities. She was known for her quick wit, infectious laughter, and ability to thrive under pressure. However, beneath her confident exterior, Sarah harbored unresolved childhood trauma related to her parents’ tumultuous divorce, which would later compound the effects of her PTSD.

The catalyst for Sarah’s PTSD was a violent home invasion that occurred two years ago. While alone in her apartment one evening, Sarah was confronted by an armed intruder who threatened her life and subjected her to physical assault before fleeing the scene. This harrowing experience left Sarah with both physical and psychological scars, fundamentally altering her sense of safety and trust in the world around her.

In the immediate aftermath of the invasion, Sarah exhibited a range of distressing symptoms that significantly impacted her daily functioning. She experienced vivid and intrusive flashbacks of the attack, often triggered by seemingly innocuous stimuli such as unexpected noises or the sight of strangers in her vicinity. These flashbacks were accompanied by intense physiological reactions, including rapid heartbeat, sweating, and trembling, mirroring the fear and helplessness she felt during the traumatic event.

Sarah’s sleep patterns became severely disrupted, plagued by recurrent nightmares that forced her to relive the invasion night after night. This chronic sleep deprivation exacerbated her daytime symptoms, leading to irritability, difficulty concentrating, and a pervasive sense of exhaustion. As a result, her work performance began to suffer, and she found herself increasingly isolated from friends and family.

PTSD Memory Loss: The Link Between Trauma and Blackouts became a significant concern for Sarah, as she struggled to recall certain aspects of the traumatic event and experienced periods of dissociation. This fragmentation of memory further contributed to her sense of disorientation and detachment from reality.

Sarah’s once-vibrant social life dwindled as she developed intense anxiety in public spaces and a deep-seated fear of being alone. She began to avoid situations that reminded her of the invasion, such as entering her apartment after dark or being in enclosed spaces with strangers. This avoidance behavior, while providing temporary relief from her anxiety, ultimately served to reinforce her fears and further limit her engagement with the world.

The cumulative effect of these symptoms led to a marked decline in Sarah’s quality of life. Her once-promising career trajectory stalled as she struggled to meet deadlines and interact effectively with colleagues. Personal relationships suffered as she withdrew from social engagements and found it increasingly difficult to connect emotionally with others. The vibrant, confident woman she once was seemed to have vanished, replaced by a shadow plagued by fear, hypervigilance, and a pervasive sense of vulnerability.

Diagnostic Process and Assessment

Recognizing the severity of her symptoms and their impact on her life, Sarah sought professional help six months after the traumatic event. The diagnostic process for PTSD involves a comprehensive evaluation that considers multiple factors and utilizes various assessment tools to ensure an accurate diagnosis.

The clinician began by conducting a thorough clinical interview, during which Sarah recounted her traumatic experience and described her ongoing symptoms in detail. This initial assessment was crucial in establishing a timeline of events and identifying the specific manifestations of Sarah’s distress. The clinician paid close attention to the duration and intensity of Sarah’s symptoms, as well as their impact on her daily functioning.

To meet the criteria for a PTSD diagnosis, as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), Sarah needed to exhibit symptoms from four distinct clusters: intrusion, avoidance, negative alterations in cognition and mood, and alterations in arousal and reactivity. The clinician carefully evaluated Sarah’s experiences against these criteria, noting her intrusive memories and flashbacks, avoidance of trauma-related stimuli, persistent negative emotions, and heightened startle response.

In addition to the clinical interview, standardized psychological assessment tools were employed to gather more objective data on Sarah’s symptoms. The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) was used to assess the frequency and severity of Sarah’s PTSD symptoms over the past month. This structured interview provided a comprehensive evaluation of her experiences and helped quantify the impact of her symptoms on various aspects of her life.

The PTSD Checklist for DSM-5 (PCL-5) was also administered as a self-report measure, allowing Sarah to rate the degree to which she was bothered by each PTSD symptom. This tool provided valuable insights into Sarah’s subjective experience of her symptoms and helped track changes in symptom intensity over time.

To assess for comorbid conditions that often co-occur with PTSD, such as depression and anxiety disorders, the clinician administered additional screening tools. The Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7) were used to evaluate Sarah’s mood and anxiety levels, respectively. These assessments revealed moderate symptoms of depression and severe anxiety, underscoring the complex interplay between PTSD and other mental health concerns.

Physical examinations and medical tests were also conducted to rule out any underlying medical conditions that might be contributing to Sarah’s symptoms or complicating her presentation. A comprehensive blood panel was ordered to check for hormonal imbalances, thyroid dysfunction, or other physiological factors that could influence her mental health. Additionally, a neurological examination was performed to assess for any potential brain injuries that may have occurred during the assault.

The diagnostic process also involved gathering collateral information from Sarah’s family members and close friends, with her consent. This additional perspective provided valuable insights into changes in Sarah’s behavior and functioning that she might not have been fully aware of or able to articulate.

Throughout the assessment process, the clinician remained attuned to the potential impact of Sarah’s childhood experiences on her current symptoms. The concept of complex PTSD, which acknowledges the cumulative effect of multiple traumatic experiences, was considered in formulating a comprehensive understanding of Sarah’s presentation.

PTSD and Personality Changes: How Trauma Impacts Personal Identity became evident as the clinician observed shifts in Sarah’s self-perception and interpersonal relationships. This understanding informed the subsequent treatment planning, emphasizing the need to address both the acute trauma of the home invasion and the underlying childhood experiences that may have heightened her vulnerability to PTSD.

Treatment Approach and Interventions

Following the comprehensive diagnostic assessment, a multifaceted treatment plan was developed to address Sarah’s PTSD symptoms and improve her overall quality of life. The approach incorporated evidence-based psychotherapeutic interventions, medication management, and complementary therapies tailored to Sarah’s specific needs and preferences.

Cognitive-Behavioral Therapy (CBT) formed the cornerstone of Sarah’s treatment plan. Specifically, Trauma-Focused CBT (TF-CBT) was employed to help Sarah process her traumatic experiences and develop more adaptive coping strategies. The therapy sessions focused on several key components:

Psychoeducation: Sarah was provided with information about PTSD, its symptoms, and the rationale behind the treatment approach. This knowledge empowered her to understand her reactions and actively participate in her recovery process.

Cognitive restructuring: The therapist worked with Sarah to identify and challenge distorted thought patterns related to the trauma. For instance, Sarah’s belief that she was unsafe at all times was examined and gradually replaced with more balanced and realistic thoughts.

Exposure therapy: A crucial aspect of Sarah’s treatment involved gradual exposure to trauma-related memories and situations. This process, known as imaginal exposure, allowed Sarah to confront her traumatic memories in a safe, controlled environment. Over time, this exposure helped reduce the intensity of her emotional reactions and decrease avoidance behaviors.

Relaxation and stress management techniques: Sarah learned various relaxation methods, including deep breathing exercises, progressive muscle relaxation, and mindfulness meditation. These techniques provided her with tools to manage anxiety and physiological arousal in her daily life.

In addition to CBT, Eye Movement Desensitization and Reprocessing (EMDR) therapy was introduced as a complementary treatment modality. PTSD Treatment Breakthrough: Reconsolidation of Traumatic Memories through EMDR has shown promising results in helping individuals process traumatic memories and reduce their emotional impact. Sarah underwent several EMDR sessions, which involved recalling traumatic memories while engaging in bilateral stimulation, typically through guided eye movements.

Medication management played a supportive role in Sarah’s treatment plan. After careful consideration and discussion with Sarah, her psychiatrist prescribed a selective serotonin reuptake inhibitor (SSRI) to help alleviate symptoms of depression and anxiety associated with her PTSD. The medication was closely monitored and adjusted as needed to optimize its effectiveness while minimizing side effects.

As part of a holistic approach to treatment, alternative therapies were explored to complement the primary interventions. Art therapy sessions provided Sarah with a non-verbal outlet for expressing her emotions and processing her experiences. These sessions allowed her to explore her trauma in a less direct manner, often revealing insights that were difficult to articulate through traditional talk therapy.

Yoga and mindfulness practices were also incorporated into Sarah’s treatment regimen. These body-based interventions helped Sarah reconnect with her physical self, improve her body awareness, and develop greater emotional regulation skills. The mindfulness component, in particular, proved valuable in helping Sarah stay grounded in the present moment and reduce the frequency of intrusive thoughts and flashbacks.

Throughout the treatment process, the importance of social support was emphasized. Sarah was encouraged to gradually reconnect with trusted friends and family members, and she eventually joined a support group for PTSD survivors. This peer support provided Sarah with a sense of community and validation, reinforcing that she was not alone in her struggles.

PTSD Care Plan: Shadow Health Approach to Effective Treatment and Management was utilized to ensure a comprehensive and individualized approach to Sarah’s care. This innovative method allowed for ongoing assessment and adjustment of the treatment plan based on Sarah’s progress and emerging needs.

Progress and Challenges During Treatment

Sarah’s journey through PTSD treatment was marked by significant progress interspersed with challenging setbacks, reflecting the complex and often non-linear nature of trauma recovery. As she engaged in therapy and implemented new coping strategies, Sarah began to experience gradual improvements in her symptoms and overall functioning.

One of the first notable milestones in Sarah’s treatment was a reduction in the frequency and intensity of her nightmares and flashbacks. Through consistent practice of relaxation techniques and the processing of traumatic memories in therapy, Sarah found that she was able to sleep for longer periods without interruption. This improvement in sleep quality had a cascading positive effect on her daytime functioning, enhancing her ability to concentrate at work and engage more fully in social interactions.

Another significant achievement was Sarah’s ability to challenge and reframe her negative thought patterns. As she progressed in cognitive restructuring exercises, she became more adept at recognizing when her thoughts were skewed by her trauma. For instance, she learned to question the automatic assumption that every stranger posed a threat, gradually allowing herself to feel more at ease in public spaces.

The exposure therapy component of Sarah’s treatment yielded particularly powerful results. Initially, Sarah struggled with intense anxiety during imaginal exposure exercises, often experiencing strong physiological reactions. However, with persistence and support from her therapist, she was able to confront her traumatic memories without being overwhelmed by them. This progress translated to real-world situations, as Sarah began to face previously avoided scenarios, such as entering her apartment alone at night or using public transportation.

Despite these positive developments, Sarah’s recovery path was not without its challenges. There were periods when external stressors, such as work deadlines or relationship conflicts, would trigger a temporary intensification of her PTSD symptoms. During these times, Sarah sometimes felt discouraged, questioning whether she was making genuine progress.

One particularly difficult setback occurred when Sarah encountered a situation that closely resembled her traumatic experience. While visiting a friend’s new apartment, she was startled by an unexpected noise in the hallway, triggering a severe panic attack. This incident shook Sarah’s confidence and temporarily increased her avoidance behaviors.

To address these challenges, Sarah’s treatment plan underwent several adjustments. The frequency of therapy sessions was temporarily increased during periods of heightened stress, providing additional support and guidance. The focus of sessions shifted to reinforce coping skills and help Sarah process setbacks as part of the normal recovery journey rather than as failures.

The medication component of Sarah’s treatment also required fine-tuning. Initially, Sarah experienced some side effects from the prescribed SSRI, including nausea and decreased libido. Her psychiatrist worked closely with her to adjust the dosage and timing of medication administration, eventually finding a balance that maximized therapeutic benefits while minimizing unwanted effects.

As treatment progressed, Sarah’s ability to implement coping strategies independently improved markedly. She became more proactive in using mindfulness techniques to ground herself during moments of anxiety and was able to challenge negative thoughts without the immediate guidance of her therapist. This growing self-efficacy was a crucial factor in Sarah’s long-term recovery and resilience.

The incorporation of art therapy into Sarah’s treatment plan proved to be particularly beneficial during challenging periods. When verbal expression felt difficult, Sarah found solace and insight through creative expression. Her artwork often revealed subconscious fears and hopes that she struggled to articulate, providing valuable material for further exploration in therapy sessions.

Throughout her treatment journey, Sarah’s support network played a vital role in her progress. As she became more open about her experiences with trusted friends and family, she found unexpected sources of understanding and encouragement. The PTSD support group she attended provided a sense of community and shared experience that was instrumental in combating feelings of isolation and self-blame.

PTSD Nursing Diagnosis and Care Plan: Evidence-Based Interventions and Management Strategies were also incorporated into Sarah’s overall treatment approach, ensuring that her physical health needs were addressed alongside her mental health concerns. This holistic perspective was crucial in managing the physiological aspects of her PTSD symptoms and promoting overall well-being.

Outcomes and Long-Term Management

After two years of intensive treatment and ongoing management, Sarah’s final assessment revealed significant improvements in her PTSD symptoms and overall quality of life. While she continued to experience occasional symptoms, their frequency and intensity had dramatically decreased, allowing her to regain a sense of control and engagement with her life.

The final evaluation using the CAPS-5 and PCL-5 showed a substantial reduction in Sarah’s PTSD symptom severity. Her scores on measures of depression and anxiety had also improved markedly, reflecting a broader enhancement in her mental health and emotional well-being. Sarah reported feeling more like her pre-trauma self, with a renewed sense of hope and resilience.

One of the most significant outcomes of Sarah’s treatment was the development of a robust set of coping strategies for ongoing symptom management. She had internalized many of the techniques learned in therapy and was able to apply them effectively in her daily life. Mindfulness practices had become a regular part of her routine, helping her stay grounded and present even in challenging situations.

Sarah’s ability to recognize and challenge trauma-related thoughts had become second nature. She was now able to quickly identify when her thinking was being influenced by her past experiences and could consciously choose more balanced and realistic perspectives. This cognitive flexibility greatly reduced the power that trauma-related triggers held over her emotional state.

The exposure work completed during treatment had a lasting impact on Sarah’s behavior. She was no longer avoiding situations or places that reminded her of the traumatic event. While she still experienced some anxiety in certain contexts, it was manageable and did not prevent her from engaging fully in life. Sarah had successfully reclaimed many of the activities she had previously abandoned, including her love for outdoor adventures and social gatherings.

In terms of her professional life, Sarah had made significant strides. Her improved concentration and emotional regulation allowed her to excel once again in her career. She had taken on new responsibilities at work and was considering pursuing a leadership position, something that would have seemed impossible at the height of her PTSD symptoms.

Sarah’s personal relationships had also undergone positive transformations. She had rebuilt connections with friends and family members from whom she had withdrawn during the most challenging periods of her PTSD. Moreover, Sarah felt more capable of forming and maintaining intimate relationships, having worked through many of the trust issues that had arisen following her trauma.

Despite these substantial improvements, Sarah and her treatment team recognized the importance of ongoing management strategies. A maintenance plan was developed to help Sarah sustain her progress and address any future challenges. This plan included:

Regular check-ins with her therapist: While the frequency of sessions had decreased, Sarah continued to meet with her therapist on a monthly basis to review her progress and address any emerging concerns.

Continued medication management: Sarah worked with her psychiatrist to find the optimal long-term medication regimen, which included a plan for potential gradual reduction in dosage over time.

Engagement in support groups: Sarah remained an active participant in her PTSD support group, finding value in both receiving and offering support to others on their recovery journeys.

Ongoing practice of coping skills: Sarah committed to maintaining her mindfulness and relaxation practices, recognizing their role in her continued well-being.

Healthy lifestyle choices: The importance of regular exercise, balanced nutrition, and adequate sleep was emphasized as part of Sarah’s holistic approach to managing her mental health.

PTSD and Diabetes: The Complex Link and Connection Explained became relevant in Sarah’s long-term management plan, as her healthcare team monitored her physical health closely, recognizing the potential long-term physiological impacts of chronic stress.

Sarah’s case study provides valuable insights into the complex nature of PTSD and the potential for recovery with appropriate treatment and support. Her journey highlights the importance of a comprehensive, individualized approach to PTSD treatment that addresses both the immediate symptoms and the broader impact of trauma on an individual’s life.

The success of Sarah’s treatment underscores the effectiveness of evidence-based interventions such as CBT and EMDR when tailored to the specific needs of the individual. It also demonstrates the value of integrating alternative therapies and holistic approaches to address the multifaceted nature of trauma recovery.

Sarah’s experience emphasizes the non-linear nature of PTSD recovery, with progress often punctuated by setbacks and challenges. This reality highlights the need for flexibility in treatment approaches and the importance of building resilience and coping skills that can be applied long after formal treatment has ended.

The role of social support in Sarah’s recovery cannot be overstated. Her ability to reconnect with loved ones and find community among fellow survivors played a crucial role in her healing process. This aspect of her journey underscores the importance of addressing the social and relational impacts of PTSD in treatment planning.

Historical Figures with PTSD: Famous Leaders Who Battled Hidden Trauma reminds us that PTSD is not a modern phenomenon and that resilience in the face of trauma has been a part of the human experience throughout history. Sarah’s story adds to this narrative, demonstrating the potential for growth and transformation in the aftermath of traumatic experiences.

As research in the field of trauma and PTSD continues to evolve, cases like Sarah’s provide valuable real-world data that can inform future treatment approaches. The success of her multifaceted treatment plan suggests that a combination of therapeutic modalities, tailored to the individual’s needs and preferences, may offer the best outcomes for PTSD recovery.

In conclusion, Sarah’s journey through PTSD treatment and recovery serves as a powerful testament to the resilience of the human spirit and the effectiveness of comprehensive, compassionate care. Her story offers hope to others struggling with the aftermath of trauma and provides valuable insights for mental health professionals seeking to enhance their approach to PTSD treatment. As we continue to deepen our understanding of trauma’s impact on the mind and body, cases like Sarah’s will undoubtedly play a crucial role in shaping the future of PTSD care and management.

References:

1. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.

2. Bisson, J. I., Roberts, N. P., Andrew, M., Cooper, R., & Lewis, C. (2013). Psychological therapies for chronic post-traumatic stress disorder (PTSD) in adults. Cochrane Database of Systematic Reviews, 12, CD003388.

3. Foa, E. B., Keane, T. M., Friedman, M. J., & Cohen, J. A. (Eds.). (2009). Effective treatments for PTSD: Practice guidelines from the International Society for Traumatic Stress Studies. New York: Guilford Press.

4. Kessler, R. C., Aguilar-Gaxiola, S., Alonso, J., Benjet, C., Bromet, E. J., Cardoso, G., … & Koenen, K. C. (2017). Trauma and PTSD in the WHO World Mental Health Surveys. European Journal of Psychotraumatology, 8(sup5), 1353383.

5. National Center for PTSD. (2019). How Common is PTSD in Adults? U.S. Department of Veterans Affairs. https://www.ptsd.va.gov/understand/common/common_adults.asp

6. Shapiro, F. (2018). Eye movement desensitization and reprocessing (EMDR) therapy: Basic principles, protocols, and procedures (3rd ed.). New York: Guilford Press.

7. van der Kolk, B. A. (2014). The body keeps the score: Brain, mind, and body in the healing of trauma. New York: Viking.

8. World Health Organization. (2018). International classification of diseases for mortality and morbidity statistics (11th Revision). https://icd.who.int/browse11/l-m/en

9. Yehuda, R., & Hoge, C. W. (2016). The meaning of evidence-based treatments for veterans with posttraumatic stress disorder. JAMA Psychiatry, 73(5), 433-434.

10. Zalta, A. K., Held, P., Smith, D. L., Klassen, B. J., Lofgreen, A. M., Normand, P. S., … & Karnik, N. S. (2018). Evaluating patterns and predictors of symptom change during a three-week intensive outpatient treatment for veterans with PTSD. BMC Psychiatry, 18(1), 242.

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We need to better support First Nations women with violence-related brain injuries. Here’s how

ARC DECRA Fellow, Institute for Culture and Society, Western Sydney University

Research Associate, Institute of Culture and Society, Western Sydney University

Disclosure statement

Michelle Fitts receives funding from the Australian Research Council and the National Health and Medical Research Council.

Elaine Wills does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Western Sydney University provides funding as a member of The Conversation AU.

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Please be advised this article contains details of family violence.

Domestic violence causes disability for women through lasting impacts on their brains.

Traumatic brain injury refers to damage to, or alteration of, brain function due to a blow or force to the head. This leads to bruising, bleeding and tearing of brain tissue.

Such injury can have short-term (acute) effects or cumulative effects (over months or years).

A 2008 study by researchers in Adelaide found Aboriginal women experience head injury – including traumatic brain injury – due to assault at 69 times the rate of non-Indigenous women.

We spoke to Aboriginal and Torres Strait Islander women and communities in regional and remote Australia about their experiences of traumatic brain injuries from violence and their decision-making about health care access.

We also spoke to family members about what they observed in other women who were important to them and had experienced traumatic brain injury.

Here’s what we found – and how it can inform the development of better health care and support services.

Not feeling like the person I used to be

Violence-related traumatic brain injuries are not isolated experiences . The Aboriginal and Torres Strait Islander women we spoke to reported repetitive, violence-related head injuries over prolonged periods. Most women reported dozens of head injuries or had lost count of the number of injuries suffered.

The violence experienced was usually from Indigenous and non-Indigenous current or former male partners.

Aboriginal and Torres Strait Islander women reported living with and managing many common changes from traumatic brain injuries, including:

• memory troubles
• dizziness and headaches
• difficulty with concentration and organisation
• trouble with taking in information and thinking (sometimes described as “mixed up thinking”)
• finding it hard to start a yarn or keep conversations going with family and friends (described as “losing the words or having the words disappear” or feeling like “my brain went blank”)
• mood swings and impulsivity.

Coral shared:

Black out, now suffering from memory loss, like finding hard to be telling a yarn. These are stories that have happened to me. But I can’t remember it.

Kirra said about her own experience:

I put something somewhere, like, book, keys, phone. If I can’t see it, I forget where I put it. I have troubles keeping focused on one thing.

The women we interviewed also frequently mentioned being strangled. Non-fatal strangulation is also harmful to the brain because it reduces blood flow to the head and deprives the brain from getting oxygen.

When and why women access health care

Women felt accessing health care and support services after violence-related traumatic brain injury was not always an option for them. This was primarily because they were experiencing coercive control or were worried they would be reported to child protection authorities .

The characteristics of the injury also influenced their decisions about accessing health care. If there was no visible bruising, lacerations or marks, blood or recalled loss of consciousness (or blacking out) many Aboriginal and Torres Strait Islander women did not go to hospital and managed their own symptoms.

As Cathy explains:

Then one night he hit me. There was no hospital, no blood, bleeding, no one would have thought there was domestic violence that happened to me. Didn’t think it was serious enough to go.

How women manage symptoms of traumatic brain injury

Aboriginal and Torres Strait Islander women practised a range of activities to help improve their memory and manage anxiety after their traumatic brain injury. This included:

• painting and weaving
• listening to meditation music
• completing puzzles and other tactile activities.

Family and friends helped Aboriginal and Torres Strait Islander women with daily activities like shopping at the supermarket, paying bills and attending appointments. As Pat shares:

Someone from the family talks to me on the phone when I’m at the shop so I don’t forget. Sometimes my daughter or grandchildren will take a photo and send to me.

However, homelessness, isolation and ongoing violence undermined many Aboriginal and Torres Strait Islander women’s capacity to seek medical care for traumatic brain injury and to use these strategies.

The need for regional and remote investment

We need to strengthen access to health care and other support services for Aboriginal and Torres Strait Islander women with violence-related traumatic brain injuries. Our research shows this should include:

developing standardised coordinated care pathway within emergency departments and remote community clinics

developing a specialised workforce with training in traumatic brain injury and violence (such as Aboriginal social workers and Aboriginal allied health workers) who can support women in the health-care setting and in the community

the inclusion of traumatic brain injury pre-screening questions in primary health and family violence screening tools, including Aboriginal and Torres Strait Islander health checks and state/territory family violence risk assessments

educational resources that raise awareness and knowledge of traumatic brain injury and non-fatal strangulation among women, families and communities. This must include information that lasting harm to the brain can occur even when the person doesn’t lose consciousness or there is no visible injury

needs-based funding for crisis accommodation services in regional towns and remote communities to ensure services can respond effectively to local need

investment in the development of concussion clinics in regional and remote Australia.

Any recommendations implemented must include local partnerships with Aboriginal and Torres Strait Islander peoples to ensure these practical measures are community-led, culturally appropriate and are beneficial overall, without doing further harm.

If this article raises issues for you or someone you know, contact 1800 RESPECT (1800 737 732) or 13YARN (13 92 76). In an emergency, call 000.

We acknowledge and thank the Aboriginal and Torres Strait Islander women whose stories appear in this article. We also acknowledge the artists who contributed to the project, including Shirleen Nampajinpa Campbell and Michelle Tyhuis.

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JSmol Viewer

A pilot study of saliva microrna signatures in children with moderate-to-severe traumatic brain injury.

1. Introduction

2. materials and methods, 3.1. stbi vs. control group, 3.2. stbi patients over time, 3.3. pathway analysis, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Ciancaglini, R.; Botash, A.S.; Armijo-Garcia, V.; Hymel, K.P.; Thomas, N.J.; Hicks, S.D. A Pilot Study of Saliva MicroRNA Signatures in Children with Moderate-to-Severe Traumatic Brain Injury. J. Clin. Med. 2024 , 13 , 5065. https://doi.org/10.3390/jcm13175065

Ciancaglini R, Botash AS, Armijo-Garcia V, Hymel KP, Thomas NJ, Hicks SD. A Pilot Study of Saliva MicroRNA Signatures in Children with Moderate-to-Severe Traumatic Brain Injury. Journal of Clinical Medicine . 2024; 13(17):5065. https://doi.org/10.3390/jcm13175065

Ciancaglini, Robert, Ann S. Botash, Veronica Armijo-Garcia, Kent P. Hymel, Neal J. Thomas, and Steven D. Hicks. 2024. "A Pilot Study of Saliva MicroRNA Signatures in Children with Moderate-to-Severe Traumatic Brain Injury" Journal of Clinical Medicine 13, no. 17: 5065. https://doi.org/10.3390/jcm13175065

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AI offers ‘paradigm shift’ in study of brain injury

From the gridiron to the battlefield, the study of traumatic brain injury has exploded in recent years. Crucial to understanding brain injury is the ability to model the mechanical forces that compress, stretch, and twist the brain tissue and causing damage that ranges from fleeting to fatal.

Researchers at Stanford University now say they have tapped artificial intelligence to produce a profoundly more accurate model of how deformations translate into stresses in the brain and believe that their approach could reveal a more definitive understanding of when and why concussion sometimes leads to lasting brain damage, and other times not.

“The problem in brain modeling to date is that the brain is not a homogeneous tissue—it’s not the same in every part of the brain. Yet, trauma is often pervasive,” said Ellen Kuhl , professor of mechanical engineering, director of the Living Matter Lab , and senior author of a new study appearing in the journal, Acta Biomaterialia . “The brain is also a ultrasoft, much like Jell-O, which makes both testing and modeling physical effects on the brain very challenging.”

Going to the library

Researchers who want to study brain trauma are forced to select from a library of dozens of material models, some dating back almost a century, to help calculate the stresses on the brain.

For decades, scientists have developed these models for soft materials with inscrutable names like the “neo-Hookean Model for Plastics and Rubber,” the “Demiray Model for Soft Tissues,” and the “Ogden Model for Rubber-like Solids.” But a model that works for a certain type of stress—tension, compression, or shear—may not work for another. Or, a model that might work for one region of the brain, might not for another.

The new approach takes a model-of-models tack, using artificial intelligence to discover which model, out of more than 4,000 possibilities, best explains the behavior of the brain. In the past, selecting the best model was a hit-or-miss process that depended largely on user experience and personal preference.

“We take user-selection out of equation by allowing machine learning to examine the data and decide which model works best,” adds Sarah St. Pierre , a doctoral scholar in Kuhl’s lab and a co-author of the paper. “Automating this process lowers barriers to model the brain. Now, every Stanford student can do this!” Once the machine learning has discovered the best model, it’s easy to relate it back to the models that generations of researchers have proposed.

Transformative insights

The approach, known as a Constitutive Artificial Neural Networks, was developed by Kevin Linka before he joined the Living Matter Lab as a postdoc to apply his method to the brain.

“We provide the network all existing constitutive models developed over the last century. And the AI does a mix and match to find the best option. This is impossible to do by hand,” Linka said. “Now, we’ve effectively discovered a new model that makes us more confident in studying the mechanical stress in the brain.”

Unlike conventional off-the-shelf neural networks, Constitutive Artificial Neural Networks provide novel insights into the physics of the brain. As but one example, the team notes that they have pinpointed physically meaningful parameters, such as varying shear stiffnesses in four different regions of the brain—the cortex, basal ganglia, corona radiata, and corpus callosum—at precisely 1.82, 0.88, 0.94, and 0.54 kilopascals each.

The shear modulus relates the force from a hit to the head, for instance, to the resulting deformation of the brain tissue. By these measures, the cortex—the gray, outer layer of the brain—is more than three times as stiff as the corpus callosum, the network of nerves connecting the two hemispheres of the brain.

With such improved knowledge, brain trauma researchers can more accurately simulate and understand where in the brain trauma originates. This could inspire the design of new protective equipment or treatments that promote healing. To translate this knowledge into engineering practice, Kuhl’s group has collaborated with a simulation software company, Dassault Systemès Simulia, to integrate automated model discovery directly into their analysis workflow.

“What’s really most exciting about this research,” Kuhl said, “is that Constitutive Artificial Neural Networks could induce a paradigm shift in soft tissue modeling, from user-defined model selection to automated model discovery. This could forever change how we simulate materials and structures.”

This work was supported by a German Academic Exchange Service (DAAD) Fellowship, by a National Science Foundation Graduate Research Fellowship, by the Stanford School of Engineering Covid-19 Research and Assistance Fund, and by a Stanford Bio-X IIP seed grant.

Related:   Ellen Kuhl , professor of mechanical engineering

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High school football player dies after suffering brain injury during game

SELMA, Ala. (WSFA/Gray News) - An Alabama community is mourning the death of a 16-year-old high school football player who was hospitalized after suffering a severe brain injury during a game.

Caden Tellier, 16, was a high school football player at Morgan Academy, a private school in Selma, Alabama. He was rushed to the hospital Friday night after suffering a brain injury during a football game against Southern Academy, WSFA reports .

Tellier died Saturday from his injuries, the school confirmed . The exact nature of the teenager’s injuries is unknown.

“It is with a heavy heart that I must inform you that Caden Tellier has gone to be with his Lord and Savior,” said Dr. Bryan Oliver, the school’s headmaster, in a statement. “Caden loved the Lord with all his heart and was a shining light every day he graced the halls of Morgan Academy. He was a student, a friend, an athlete and, most important, a Christ follower.”

Oliver also offered support and sympathy for Tellier’s family.

Tellier was known for “his kindness, generosity and love,” according to a GoFundMe set up for his family . His organs will be donated.

“True to his nature, he is giving of himself one more time to save the lives of others through the donation of his organs. His legacy will live on forever, and we thank God that we had the opportunity to love him and be loved by him,” the GoFundMe reads.

The Alabama Independent School Association, of which Morgan Academy is a part, released a statement earlier Saturday regarding Tellier’s injury.

“We ask that our entire AISA family and the people of Alabama join us in prayer for peace and comfort for Caden’s family and the Morgan Academy community as they navigate this difficult time,” said Michael McLendon, the AISA’s executive director.

McLendon also noted that Morgan Academy has chosen to suspend all school activities this week to give the community time to come together.

Copyright 2024 WSFA via Gray Local Media, Inc. All rights reserved.

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PRIME Study Progress Update — Second Participant

Last month, Alex,* the second participant in our PRIME Study,** received his Neuralink implant (Link). The surgery, conducted at the Barrow Neurological Institute , went well — Alex was discharged the following day, and his recovery has been smooth. With the Link, he has been improving his ability to play video games and began learning how to use computer-aided design (CAD) software to design 3D objects. This marks another significant step towards providing a high-performance interface that will enhance the control of digital devices for people with quadriplegia to help restore their autonomy.

The overarching aim for the PRIME Study is to demonstrate that the Link is safe and useful in daily life, as noted in our last blog post . In this blog post, we share updates from the experience of our second participant across three key dimensions that support this aim:

• Out-of-the-box experience
• Repertoire of capabilities
• Thread retraction mitigations

Out-of-the-Box Experience

From the first moment Alex connected his Link to his computer, it took less than 5 minutes for him to start controlling a cursor with his mind. Within a few hours, he was able to surpass the maximum speed and accuracy he’d achieved with any other assistive technology on our Webgrid task. Similar to Noland, our first participant, Alex broke the previous world record for brain-computer interface (BCI) cursor control with a non-Neuralink device on day one of using the Link. After the first research session concluded, Alex continued testing the capabilities of the Link independently, using it to play the first-person shooter game Counter-Strike .

“I’m already super impressed with how this works.” — Alex, PRIME Study participant

Alex using the Link to play  Webgrid .

Repertoire of Capabilities

Alex enjoys building things. Before his spinal cord injury, he worked as an automotive technician, fixing and tinkering with various types of vehicles and large machinery. Since then, he has wanted to learn how to design 3D objects using computer-aided design (CAD) software so he could work on projects without needing to rely extensively on his support system. However, the level of control offered by his assistive technologies made this challenging.

On day two of using the Link, Alex used the CAD software Fusion 360 for the first time and managed to design a custom mount for his Neuralink charger , which was then 3D printed and integrated into his setup. We are working with Alex to increase his productivity with the Link by mapping intended movements to different types of mouse clicks (e.g., left, right, middle), thereby expanding the number of controls he has and enabling him to quickly switch between various modes in CAD software (e.g., zoom, scroll, pan, click-and-drag). 

In his free time, Alex continues to use CAD software to turn his design ideas into reality. We hope that in time, the Link helps many people create in their areas of interest and expertise, and we’re excited to work with more people to help them reconnect with their passions.

“Taking an idea, putting it as a design, and actually having a physical item as a finished product makes me feel like I’m building things again.”  — Alex, PRIME Study participant

In this video, Alex uses his Link to carve out the center of the custom mount for his Neuralink charger (finished product shown in Fig 03). On the right side of his screen is a mode switcher — a user interface element developed by Neuralink — which he leverages to quickly change the functionality of his mouse.

Alex also enjoys playing first-person shooter games, which generally require the use of numerous inputs, including two separate joysticks (one for aiming and the other for moving) and an array of buttons. Before receiving the Link, Alex enjoyed playing these games using an assistive device called the Quadstick — a mouth operated joystick with sip-and-puff pressure sensors and a lip position sensor for clicking. However, a key limitation of the controller is that it only has one joystick, restricting Alex to either moving or aiming at any given time. Switching from moving to aiming involves letting go of the joystick and then sipping or puffing into a separate straw to toggle the functionality. Now, Alex is able to use the Link in combination with his Quadstick to move and aim simultaneously, unlocking a more intuitive gameplay experience. 

“Just running around is so enjoyable because I can look side to side, and not need to move Quadstick left and right… I can [think about where to] look and it goes where I want it to. It's insane.” — Alex, PRIME Study participant

Alex playing Counter-Strike.

Thread Retraction Mitigations

With our first participant, Noland, we observed a degree of thread retraction that temporarily reduced his BCI performance. The threads have stabilized, and the performance of Noland’s Link has since recovered — more than doubling the prior world record for BCI cursor control. 

To reduce the probability of thread retraction in our second participant, we implemented a number of mitigations, including reducing brain motion during the surgery and reducing the gap between the implant and the surface of the brain. We discussed these measures in greater detail in our live update prior to our second participant’s surgery. 

Promisingly, we have observed no thread retraction in our second participant.

Looking Forward

To further enhance our participants’ experience using their digital devices, we are continuing to expand the controls that are available to them. We are working on decoding multiple clicks and multiple simultaneous movement intents to deliver full mouse and video game controller functionality. We are also developing algorithms to recognize handwriting intent to enable faster text entry. These capabilities would not only help restore digital autonomy for those who are unable to use their limbs, but also restore the ability to communicate for those who are unable to speak, such as people with neurological conditions like amyotrophic lateral sclerosis (ALS) .

Additionally, we plan to enable the Link to interact with the physical world, allowing users to feed themselves and move more independently by controlling a robotic arm or their wheelchair.

“The Link is a big step on the path of regaining freedom and independence for myself.” — Alex, PRIME Study participant

Join the Neuralink Community

If you are excited to restore autonomy to those with unmet medical needs, consider applying to our open roles . If you are interested in shaping the future of assistive technologies by participating in a Neuralink clinical trial, please join our Patient Registry .

* Name shared at the request of the participant .

** The PRIME Study — an investigational medical device trial for our fully implantable, wireless brain-computer interface (BCI) — aims to evaluate the safety of our implant and surgical robot, and assess the initial functionality of our BCI for enabling people with quadriplegia to control external devices with their thoughts. We do not guarantee any benefit by participating in the PRIME Study.

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Psychological Intervention in Traumatic Brain Injury Patients

Lizzette gómez-de-regil.

1 Hospital Regional de Alta Especialidad de la Península de Yucatán, Calle 7, No. 433 por 20 y 22, Fraccionamiento Altabrisa, Mérida, Yucatán, 97130, Mexico

Damaris F. Estrella-Castillo

2 Autonomous University of Yucatan, School of Rehabilitation, Avenida Itzáes No. 498 x 59 y 59A, Colonia Centro, Mérida, Yucatán, C.P. 97000, Mexico

Julio Vega-Cauich

3 Foco Rojo-Centro de Psicología Aplicada, Calle 47, No. 506 por 62 y 64, Colonia Centro, Mérida, Yucatán, C.P. 97000, Mexico

To provide a brief and comprehensive summary of recent research regarding psychological interventions for patients surviving a traumatic brain injury.

A bibliographical search was performed in PubMed, Cochrane Library, PsycNET, Scopus, ResearchGate, and Google Scholar online databases. Analysis included distribution by year of publication, age stage of participants (paediatric, adult), location of the research team, study design, type of intervention, and main outcome variables.

The initial search eliciting 1541 citations was reduced to 62 relevant papers. Most publications had adult samples (88.7%). The United States outstands as the country with more research (58.1%); Latin America countries provided no results. Cognitive behavioural therapy (CBT) was the most widely used approach for treatment of (sub)clinical mental disturbances (41.9%). Neuropsychological interventions were scarce (4.8%). Outcome measures included psychiatric disorders (e.g., posttraumatic stress disorder (PTSD), depression, and anxiety) (37.1%), postconcussive symptoms (16.1%), cognitive and functional deficits (48.1%), and social and psychological dimensions (62.9%).

Conclusions

CBT outstands as the preferred therapeutic approach for treating behavioural and emotional disturbances. Also, other related therapies such as dialectical behaviour, mindfulness, and acceptance and commitment therapies have been proposed, and probably in the years to come, more literature regarding their effectiveness will be available. On the other hand, evidence showed that interventions from the field of neuropsychology are minimal if compared with its contribution to assessment. Future research should be aimed at performing studies on more diverse populations (e.g., nonmilitary communities and paediatric and Latin American populations) and at controlling designs to examine the therapeutic efficacy of psychotherapeutic and neurocognitive rehabilitation interventions and compare amelioration by injury severity, age of patients, and clinical profile, in the hopes of creating better guidelines for practitioners.

1. Introduction

Traumatic brain injury (TBI) is a disruption in normal brain function caused by external mechanical force, such as rapid acceleration or deceleration, a bump or jolt to the head, or penetration by a projectile. As an acquired brain injury (i.e., postnatal brain damage), TBI is differentiated from nontraumatic brain injuries not involving an impact from external forces (e.g., those caused by strokes and infections). Considering symptom severity and duration (loss of consciousness, posttraumatic amnesia, and memory and motor deficits), TBI can be classified as concussion, mild, moderate, or severe [ 1 , 2 ].

Someone with TBI, even if medically stable, is likely to experience subsequent symptoms ranging from physical (headache, fatigue, and visual/auditory sensitivity) to cognitive (deficits in memory, attention, concentration, and executive function) and emotional (depression, anxiety) symptoms [ 1 ]. Various treatment modalities have been proposed and tested, from medical/surgical to behavioural/cognitive methods (see reviews [ 3 – 5 ]). Addressing impairments that cut across multiple disciplines requires assessment and rehabilitation following an interdisciplinary model with a team of experts on physical medicine and rehabilitation, speech-language pathology, social work, and (neuro)psychology, among others [ 6 ].

Current TBI therapies include pharmacotherapy, psychotherapy, and cognitive rehabilitation. However, psychological and emotional issues often remain overlooked even when physical, behavioural, and cognitive symptoms are treated [ 7 ]. Psychology has a long history of research and practice on neuropsychological assessment of TBI patients, and there is a growing interest in designing, testing, and providing suitable psychological interventions.

Psychology has contributed to TBI patient care mainly from a neuropsychological perspective. Neuropsychology is a hybrid science in which psychology, psychiatry, and neurology converge in the study of connections between the brain and behaviour. Assessment has been its main role. This is done through techniques and instruments aimed at evaluating patient neurocognitive, behavioural, and emotional strengths and weaknesses and interpreting their link to brain anatomy and function [ 8 ]. Beyond clinical diagnosis for treatment planning and progress, neuropsychological assessment has also become a core aspect of decision-making regarding function and disability in legal [ 9 ], labour [ 10 ], and sports [ 11 , 12 ] contexts.

As a result of TBI, cognitive (e.g., deficits in attention, memory, and executive function) and behavioural (e.g., aggression, poor impulse control, irritability, anhedonia, or apathy) symptoms may occur and psychiatric/affective disorders may initiate or worsen [ 13 ]. Beyond neuropsychological assessment, psychologists have also worked intensively on the design, implementation, and testing of post-TBI interventions. Psychology has aided in the cognitive rehabilitation of TBI patients [ 14 , 15 ], as well as in helping them to manage the emotional impact of this condition through psychotherapy [ 16 ] or psychoeducational programs [ 15 ]. Family interventions are another technique applied to TBI survivors since the condition can adversely impact relatives, who often play a critical supporting role in the patient recovery process [ 17 ].

Given their particular health conditions, TBI survivors may require professional psychological support to deal with both cognitive and emotional challenges. This review is aimed at providing a brief and comprehensive summary of recent research on psychological interventions in TBI survivors that are potentially of interest to professionals working with this population.

A bibliographical search was performed in the PubMed, PsycNET, Web of Science, Scopus, Cochrane Library, and Google Scholar databases. The terms “traumatic brain injury” and “TBI” were entered in combination with “psychology”, “neuropsychology”, “psychoeducation”, and “psychotherapy”. Filters were applied to retrieve only articles published in English during the decade prior to the search (2008 to July 2018). Online resources were accessed on 25 to 27 July 2018. Publication relevance was verified based on the study objective. Citations for publications other than research articles (e.g., commentary, erratum, and editorials) were excluded, as were articles reporting (systematic) reviews and/or meta-analyses. Abstracts were used to make a further cull of publications reporting research not clearly related to patients with TBI and/or not focused on psychological intervention. Once a final reference list was generated, a series of data were collected on each article: year of publication, participant age stage (paediatric, adult), research team location, study design, intervention type, and main outcome variables. All three authors worked together during the bibliographic research procedure, and discrepancies were minimal.

The initial search from the six selected databases produced 1,541 citations, of which 617 were duplicates, three were not within the specified publication year range, and seven were not in English. After applying the exclusion criteria and reviewing the available abstracts, the list was reduced to 62 relevant publications ( Figure 1 ).

Study flow diagram.

Classification by participant age stage, year, location, and study design showed that most of the studies have been done using adult samples ( n = 55, 88.7%), with participants aged 16 to 73 ( Table 1 ). Only a small portion ( n = 7, 11.3%) involved paediatric samples; participants were 4 to 18 years old, and the studies were done in the United States ( n = 5) and Italy ( n = 2). The number of relevant publications increased notably during the last three years of the publication time range and accounted for 41.9% ( n = 26) of the results; no results were found for the year 2010. Most of the studies are from the United States ( n = 36, 58.1%) and were done with adult samples ( n = 31, 86.1%). Over half the studies performed with adults (54.8%) included veterans or active-duty service members, either exclusively ( n = 15) or in combination with civilians ( n = 2). The research from all the other countries was done with nonmilitary populations. Fifteen of the remaining studies are from Europe and were done in Finland, France, Italy, the Netherlands, Norway, Slovenia, Switzerland, and the United Kingdom; two studies are from Asia, one from India, and the other from Malaysia.

Distribution of publications on psychological interventions for patients with traumatic brain injury.

In terms of study design, four protocols describe 2-group ( n = 3) and 3-group ( n = 1) randomized control trials; these were done in the United States, Australia, Norway, and Finland. The case-study reports only involved adults (8 civil cases and 3 veterans). One qualitative study analysed patient interaction content to provide feedback to therapists and improve their performance. One-group quasiexperimental designs ( n = 13) were mostly tested in community samples ( n = 7), and four of these were pilot studies. Most of the randomized control trials were two-armed ( n = 27), but there were also 3-armed ( n = 4) and four-armed ( n = 1) studies; two were pilot studies and three included wait list control groups. Only one two-armed study was a nonrandomized trial.

Two types of interventions were used: cognitive behavioural therapy (known as CBT) was the intervention technique of choice ( n = 26, 41.9%), followed by psychoeducation ( n = 16, 25.8%). Very few publications addressed neuropsychological interventions in TBI patients ( n = 3, 4.8%). Outcome measures were diverse, and most studies included various domains, such as psychiatric disorders (e.g., PTSD, depression, and anxiety) ( n = 23, 37.1%), postconcussive symptoms ( n = 10, 16.1%), cognitive and functional deficits ( n = 30, 48.1%), and social and psychological aspects ( n = 39, 62.9%) ( Table 2 ).

Types of psychological interventions and their main outcome measures.

Note: a work may include more than one type of intervention and/or outcome measure.

Of note, not all the reported interventions were done following standard, face-to-face techniques. Limited access to psychological services has fuelled increasing interest in implementing technology to broaden intervention options. Some studies involved telephone-based [ 18 – 21 ] or computer-based [ 22 , 23 ] interventions, while others employed diverse technologies such web-based programs [ 24 , 25 ], mobile applications [ 26 ], videogames [ 27 ], and virtual reality [ 28 ].

4. Discussion

This review is a brief, comprehensive overview of scientific manuscripts reporting on psychological treatments applied to TBI survivors with the purpose of helping them to directly or indirectly overcome cognitive and emotional issues linked to their physical condition.

Once a TBI patient is physically stable, subsequent cognitive, emotional, behavioural, and social difficulties may manifest, hindering engagement with treatment and daily activities. Managing these challenges requires a comprehensive neuropsychological treatment approach. As the most widely used psychotherapeutic approach, CBT is built on the assumption that cognitions (i.e., thoughts) strongly affect behaviours, but, through awareness, can be quantified and controlled. In other words, a person can attain behavioural changes through acknowledgment and control of preceding cognitions. Application of CBT for TBI patients has been aimed at reducing anger, depression, anxiety, and PTSD symptoms and at improving coping, with promising results [ 29 , 30 ]. However, adaptations are still needed for this population to improve intervention efficacy and allow replication [ 31 ]. If the aim of a multidisciplinary team is to achieve the best possible outcome, the medical professionals involved need information on the aims and techniques of psychotherapy whereas the psychotherapists need to understand the disorder's medical characteristics. Psychotherapy with TBI patients can be challenging and frustrating at times but is worth attempting since it can be very rewarding for both the survivor and therapist [ 30 ].

Research on psychological interventions in TBI patients has grown over the last decade and boomed during the last three years. The United States is the apparent leader in this research area since TBIs have been acknowledged as an important public health issue. Estimates from the United States indicate that TBIs annually account for approximately 2.5 million emergency room visits, hospitalizations, and deaths nationwide; however, this does not include sufferers who did not receive medical care, had outpatient or office-based visits, or were treated at a federal facility (e.g., active-duty military members and veterans). Those who have served in the United States military are at significant risk for TBI; for instance, an estimated 4.2% of veterans from the Army, Air Force, Navy, or Marine Corps have been diagnosed with TBI [ 32 ]. These particular circumstances may account for the development of various psychological interventions in this country for both research and clinical practices. Research in this area has also been published for populations in Australia, North America, Europe, and Asia. Of note, no results were obtained from Latin America. Researchers and clinicians from this region could benefit greatly from sharing their knowledge on psychological intervention as an element in TBI treatment; initially, this could be done through qualitative and quasi experimental designs requiring little infrastructure.

As observed in a previous review of this area [ 33 ], the present results indicated that research in paediatric patients with TBI has been less frequent than that in adults. Although TBIs in children are less frequent, they imply a higher risk of negative impact given that physical and cognitive development are still very much in the process in children. Interventions in paediatric populations also bring additional challenges. For instance, neurocognitive skills are not as fully established as in adults, development is not homogeneous throughout childhood, and the younger the patient the more therapy relies on parents.

Treating patients with psychiatric and neurocognitive symptomatology, which occurs in some TBI cases, can present a unique challenge. Progress in psychotherapy can be significantly hindered by cognitive deficits, and the effectiveness of neurocognitive rehabilitation can be diminished by psychiatric overlay [ 34 ]. Ample research is available on neuropsychological assessment of TBI patients, but the present review highlights that publications on neuropsychological interventions are scarce and largely focus on validating specific cognitive rehabilitation techniques. For example, one study in paediatric patients explored the effect of a one-time neuropsychological consultation on postconcussive symptoms [ 35 ], while another tested an intervention specifically designed to improve attention, working memory, and executive function [ 36 ]. The scientific contribution of neuropsychology to clinical assessment of TBI patients will no doubt continue providing valid and efficient measurements. Neuropsychologists now need to apply themselves to designing, implementing, and testing novel cognitive rehabilitation interventions that, together with neuropsychological evaluations, provide patients with the most adequate treatment.

In terms of outcomes, most of the reviewed studies included mental health variables ranging from symptoms below the clinical threshold to diagnosed mental disorders such as depression, anxiety, or PTSD. Regardless of the approach, be it psychotherapy or neuropsychological rehabilitation, all psychological interventions in TBI patients ideally need to consider outcome variables from both fields, including examination for mental disorders and evaluation of cognitive functioning. The use of scales facilitates assessment of patients for clinical and research purposes, although most outcome scales for TBI are functional measures. As the emotional, cognitive, psychosocial, and health-related quality of life aspects of recovery are increasingly recognised, metrics to assess these domains are becoming essential [ 37 – 39 ]. Clinicians and researchers require reliable, valid measures to comprehensively quantify the level of burden and functional impairment in TBI survivors in the short and long terms. These will improve patient care by allowing proper diagnosis, prompt assignment to rehabilitation, and accurate assessment of intervention impact [ 37 , 39 ].

In neurological disorders, including TBI, biomarkers play an important role in research and clinical practice by allowing physiological processes to be monitored in health and sickness [ 40 ]. Magnetic resonance imaging provides several measurement options that can function as TBI biomarkers, including detection of hemosiderin and white matter abnormalities, assessment of white matter integrity derived from diffusion tensor imaging, and quantitative measurements that directly assess neuroanatomy. Magnetic resonance could also be a useful biomarker in individuals who, having survived TBI, have recovered without neuroimaging signs or neuropsychological effects detected with current methods [ 41 ]. Blood biomarkers have also been proposed recently as surrogate markers to improve care quality and reduce diagnosis costs [ 42 ]. Evidence-based treatments (i.e., pharmacological or nonpharmacological interventions) of TBI are currently extremely limited, and further research is needed including prospective, longitudinal studies to explore biomarkers along with standard outcome measures [ 43 ].

As a final note, TBI severity is an important factor to consider when selecting patients for a specific intervention and for assessing outcome. Because the studies included in this review relied on diverse TBI severity scales (e.g., Glasgow Coma Scale, Barell Matrix, and Abbreviated Injury Scale), some omitted reporting the classification criteria and others did not specify the level of severity and therefore did not analyse this variable.

5. Conclusions

This brief overview of recent research on psychological interventions for TBI patients showed that CBT is the preferred therapeutic approach for treating behavioural and emotional disturbances. Other related therapies such as dialectical behaviour, mindfulness, and acceptance and commitment therapies have been proposed, and the literature regarding their effectiveness is sure to grow in the coming years. When compared to its contribution to TBI assessment, neuropsychology is used minimally for interventions. Psychotherapeutic and neurocognitive rehabilitation interventions for TBI patients are challenging for both clinicians and researchers. Future research needs to include more diverse populations (e.g., nonmilitary communities and paediatric and Latin American populations). In addition, it should focus on controlled designs to examine the therapeutic efficacy of psychotherapeutic and neurocognitive rehabilitation interventions and compare amelioration by injury severity, patient age, and clinical profile in the hopes of creating the best practice guidelines for practitioners.

Acknowledgments

This work was supported by the “Hospital Regional de Alta Especialidad de la Península of Yucatán.”

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this article.

Even mild concussions can have long-lasting effects on brain and behavior

New research has found that even a years-old mild concussion can have long-lasting effects on brain function and behavior in otherwise healthy people. The study adds to a growing understanding of traumatic brain injury and is relevant to the evolving legal landscape around brain injuries in sports.

A concussion is a mild form of traumatic brain injury (TBI) resulting from events like falls, car crashes, contact sports, or assaults. The resulting disruption to brain function is often thought to be temporary. However, evidence is mounting that TBI is a risk factor for dementia , prompting research led by the University of Cambridge in the UK to investigate how the brain fares in the long term following a TBI, even a mild one.

They recruited 617 healthy middle-aged UK adults aged 40 to 59 as part of the Prevent Dementia study. Participants underwent MRI scans and neuropsychological testing to assess brain structure and function and their TBI history was assessed using the Brain Injury Screening Questionnaire (BISQ). TBI was defined as having experienced at least one blow to the head resulting in a loss of consciousness. Mild TBI was defined as a loss of consciousness of less than 30 minutes. The risk of cardiovascular disease was also assessed.

Of the 617 participants, 36.1% reported at least one TBI with a loss of consciousness. Of those, 56.1% reported a single TBI event, 27.4% reported two TBI events, and 16.6% reported more than two. Of the 223 participants with a history of TBI, injury severity was determined for 76.2%, of which 94.1% reported a mild TBI and 5.9% reported moderate-severe incidents that included a loss of consciousness of 30 minutes or longer.

Cerebral microbleeds – small, chronic brain hemorrhages – were detected in about one in six participants (17.7%). Compared to those without a TBI, the number of microbleeds was greater in participants with prior TBI, including those with mild TBI. Greater numbers of TBI events were associated with poorer sleep, gait disturbances, greater depression symptoms, and memory deficits but not deficits in attention. The mild TBI group had poorer sleep, depression, and gait but no cognitive effects.

The researchers examined the relative contribution of TBI and cardiovascular risk factors (for example, high blood pressure, diabetes) to these clinical deficits and found that TBI was the most important factor contributing to depression and sleep (but not cognition or gait), outweighing the contribution of cardiovascular risk factors. While TBI dominated cardiovascular risk factors in contributing towards memory deficits, the main dominating factors were sex and age.

“These data demonstrate that in otherwise healthy middle-aged adults, remote TBI history was associated with detectable changes in vascular brain imaging and clinical features,” said the researchers. “Overall, our findings have important implications for future research directions, as well as informing clinical practices and policymaking at the community level.”

In terms of informing clinical practices, the researchers say that undertaking TBI assessments in circumstances where someone is known to have had a brain injury could help ascertain which patients are at higher risk and enable treatment of their symptoms earlier.

The issue of TBI in sports has become a major concern in recent years, as more and more evidence has emerged about the short-, medium-, and long-term consequences of damage caused by such injuries. Most examples come from contact sports like boxing and martial arts, soccer, hockey, and football and, as a result, the legal landscape around this issue has changed.

Brain injury lawsuits in sports have become increasingly common, with athletes holding organizations and individuals accountable for the injuries they sustain while playing. In 2015 in the US, the NFL, without admitting any wrongdoing, reached a class action settlement , promising to pay compensation to former players who’d been diagnosed with dementia or other brain diseases associated with concussions. According to The Washington Post , since the NFL Concussion Settlement was finalized, it’s paid out almost US$1.2 billion to more than 1,600 former players and their families, which is far more than experts predicted during settlement negotiations. As of the 19th of August , there were 20,572 registered settlement class members.

In the UK, a class action is currently underway in the various rugby codes, targeting three of the sport’s governing bodies. In the lawsuit, 295 ex-players – amateur and professional men and women ranging in age from 22 to 80 – are alleging that World Rugby, England’s Rugby Football Union and the Welsh Rugby Union failed to put in place reasonable measures to protect the health and safety of players.

The issue is a hot topic in Australia, too. In April this year , Australian Football League (AFL) player Nathan Murphy announced his retirement from the sport – at age 24. Murphy retired on the advice of a medical panel that he consulted after suffering the tenth concussion of his sporting career. However, Murphy’s not the first, nor the youngest, player to retire early from the AFL because of head trauma. In 2016, then-22-year-old Justin Clarke left the game after a concussion resulted in weeks of memory loss. Two class action suits were filed in the Victorian Supreme Court in 2023 against the AFL, alleging concussions suffered during training and/or matches.

Then there is the controversial issue of chronic traumatic encephalopathy (CTE), a progressive neurodegenerative disease associated with repetitive head trauma. It’s controversial because a definitive causal link between CTE and contact sports hasn’t been established, although studies strongly suggest one. One such study on former NFL players with mood, behavioral, or cognitive symptoms found that 99% showed signs of CTE. A problem is that the condition can only be diagnosed at autopsy.

These often complex and emotive legal cases have ushered in a new era of safety in contact sports. But the common question that arises from all of them is this: Will sporting bodies adapt their safety rules or amend concussion protocols to better manage risk and avoid future lawsuits?

A cultural change is also required. While receiving submissions for its recently-published report, Concussions and repeated head trauma in contact sports , the Australian Parliament’s Senate Standing Committee on Community Affairs heard evidence of concussion under-reporting from sportspeople who feared that they’d be told not to play or would let their team down. Athletes’ prioritization of a ‘win at all costs’ attitude over long-term well-being needs to change, too.

The University of Cambridge-led study was published in the journal JAMA Network Open .

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• Published: 28 August 2024

Fibrin drives thromboinflammation and neuropathology in COVID-19

• Jae Kyu Ryu   ORCID: orcid.org/0000-0002-3992-4011 1 , 2 , 3   na1 ,
• Zhaoqi Yan   ORCID: orcid.org/0000-0001-9326-2920 1 , 2   na1 ,
• Mauricio Montano   ORCID: orcid.org/0000-0002-0353-0037 4 , 5   na1 ,
• Elif G. Sozmen 1 , 2 , 3   na1 ,
• Karuna Dixit   ORCID: orcid.org/0000-0003-3583-2512 1 , 2   na1 ,
• Rahul K. Suryawanshi   ORCID: orcid.org/0000-0001-8374-669X 4   na1 ,
• Yusuke Matsui   ORCID: orcid.org/0000-0002-6016-2867 4 , 5 ,
• Ekram Helmy 4 , 5 ,
• Prashant Kaushal 6 , 7 ,
• Sara K. Makanani 6 , 7 ,
• Thomas J. Deerinck 8 ,
• Anke Meyer-Franke 2 ,
• Pamela E. Rios Coronado 9 ,
• Troy N. Trevino 1 , 2 ,
• Min-Gyoung Shin 10 ,
• Reshmi Tognatta   ORCID: orcid.org/0000-0001-7460-1843 1 , 2 ,
• Yixin Liu   ORCID: orcid.org/0000-0003-1135-2805 1 , 2 ,
• Renaud Schuck 1 , 2 ,
• Lucas Le   ORCID: orcid.org/0009-0005-7794-5736 1 , 2 ,
• Hisao Miyajima 1 , 2 ,
• Andrew S. Mendiola   ORCID: orcid.org/0000-0002-2897-3299 1 , 2 ,
• Nikhita Arun 1 , 2 ,
• Brandon Guo 1 , 2 ,
• Taha Y. Taha   ORCID: orcid.org/0000-0002-7344-7490 4 , 5 ,
• Ayushi Agrawal   ORCID: orcid.org/0000-0003-2940-8926 10 ,
• Eilidh MacDonald 1 , 2 ,
• Oliver Aries 1 , 2 ,
• Aaron Yan 1 , 2 ,
• Olivia Weaver 1 , 2 , 11 ,
• Mark A. Petersen   ORCID: orcid.org/0000-0003-1366-1353 1 , 2 , 11 ,
• Rosa Meza Acevedo 1 , 2 ,
• Maria del Pilar S. Alzamora   ORCID: orcid.org/0000-0001-8861-387X 1 , 2 ,
• Reuben Thomas 10 ,
• Michela Traglia 10 ,
• Valentina L. Kouznetsova 12 , 13 ,
• Igor F. Tsigelny 1 , 12 , 13 , 14 ,
• Alexander R. Pico   ORCID: orcid.org/0000-0001-5706-2163 10 ,
• Kristy Red-Horse   ORCID: orcid.org/0000-0003-1541-601X 9 , 15 , 16 ,
• Mark H. Ellisman   ORCID: orcid.org/0000-0001-8893-8455 8 , 14 ,
• Nevan J. Krogan 10 , 17 , 18 , 19 ,
• Mehdi Bouhaddou 6 , 7 ,
• Melanie Ott   ORCID: orcid.org/0000-0002-5697-1274 4 , 5 , 19 , 20 , 21 ,
• Warner C. Greene   ORCID: orcid.org/0000-0001-9896-8615 4 , 5 , 20 , 22 &
• Katerina Akassoglou   ORCID: orcid.org/0000-0002-2632-1465 1 , 2 , 3  

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• Neuroimmunology

Life-threatening thrombotic events and neurological symptoms are prevalent in COVID-19 and are persistent in patients with long COVID experiencing post-acute sequelae of SARS-CoV-2 infection 1 , 2 , 3 , 4 . Despite the clinical evidence 1 , 5 , 6 , 7 , the underlying mechanisms of coagulopathy in COVID-19 and its consequences in inflammation and neuropathology remain poorly understood and treatment options are insufficient. Fibrinogen, the central structural component of blood clots, is abundantly deposited in the lungs and brains of patients with COVID-19, correlates with disease severity and is a predictive biomarker for post-COVID-19 cognitive deficits 1 , 5 , 8 , 9 , 10 . Here we show that fibrin binds to the SARS-CoV-2 spike protein, forming proinflammatory blood clots that drive systemic thromboinflammation and neuropathology in COVID-19. Fibrin, acting through its inflammatory domain, is required for oxidative stress and macrophage activation in the lungs, whereas it suppresses natural killer cells, after SARS-CoV-2 infection. Fibrin promotes neuroinflammation and neuronal loss after infection, as well as innate immune activation in the brain and lungs independently of active infection. A monoclonal antibody targeting the inflammatory fibrin domain provides protection from microglial activation and neuronal injury, as well as from thromboinflammation in the lung after infection. Thus, fibrin drives inflammation and neuropathology in SARS-CoV-2 infection, and fibrin-targeting immunotherapy may represent a therapeutic intervention for patients with acute COVID-19 and long COVID.

Long COVID has emerged as a central public health issue that remains an unmet clinical need 4 . Coagulation and neurological complications in COVID-19 can occur during acute infection and persist in long COVID causing morbidity and mortality 1 , 2 , 3 , 4 , 11 . Notably, coagulopathy also occurs in young patients with COVID-19 with mild infections, breakthrough infections and long COVID, and is associated with neurological complications 3 , 4 , 5 , 6 , 7 , 12 . Blood clots in patients with COVID-19 remain resistant to degradation despite adequate anticoagulation 1 , 13 , 14 . The prevalence and severity of coagulopathy and its correlations with the immune response and neurological complications in long COVID suggest as yet unknown mechanisms of COVID-19 pathogenesis.

Hypercoagulability in COVID-19 is associated with extensive fibrin deposition in inflamed lung and brain 8 , 9 , 10 . Fibrin is derived from the soluble blood protein fibrinogen after activation of coagulation and forms the central structural component of blood clots 15 , 16 . Fibrin is deposited at sites of vascular damage or blood–brain barrier (BBB) disruption, and is a key proinflammatory and prooxidant activator of the innate immune response in autoimmune, inflammatory and neurodegenerative diseases 15 , 17 , 18 , 19 , 20 , 21 . Neurovascular injury and reactive microglia are detected at sites of parenchymal fibrin deposition in brains of patients with COVID-19 8 , 9 . BBB disruption correlates with brain fog in long COVID, and increased plasma fibrinogen is a predictive biomarker of cognitive deficits after COVID-19 1 , 5 , 22 . However, the role of blood clots in COVID-19 inflammation and neurological changes remains largely unclear, and therapies to combat their effects are not readily available.

Here we provide evidence for a fundamental role of fibrinogen in the COVID-19 immune response and neuropathology, and identify a potential antibody-based strategy to combat the deleterious effects of abnormal blood clots in acute and long COVID.

Fibrinogen binds to SARS-CoV-2 spike

Given that patients with COVID-19 have a higher frequency and severity of abnormal blood clots than other common respiratory viral infections 1 , 23 , we hypothesized that SARS-CoV-2 directly binds to fibrinogen, promoting blood clot formation and altering clot structure and function. A solid-phase binding assay revealed binding of fibrinogen and fibrin to the SARS-CoV-2 recombinant trimeric spike protein (spike) and to the spike S1(N501Y) mutant, which enhances SARS-CoV-2 transmission and binding to mouse angiotensin-converting enzyme 2 (ACE2) 24 (Fig. 1a,b and Extended Data Fig. 1a ). The affinity of spike binding to fibrin (390 nM for trimeric Wuhan spike and 98 nM for spike S1(N501Y)) was lower than that of spike binding to ACE2 (1–15 nM range) 24 . Fibrinogen immunoprecipitated with full-length recombinant trimeric spike (Fig. 1c ). Fibrinogen and spike co-localized in the lungs after either intranasal (i.n.) infection of mice with mouse-proficient SARS-CoV-2 Beta (B.1.351) (Fig. 1d and Extended Data Fig. 1b,c ) or intravenous (i.v.) co-injection, into wild-type (WT) mice, of Alexa 647–spike S1(N501Y) and Alexa 546–fibrinogen, as shown by 3D imaging of solvent-cleared organs (3DISCO) 20 of cleared lungs (Extended Data Fig. 1d ), suggesting that fibrin/fibrinogen and spike interact in solution and in tissues.

a , b , Binding enzyme-linked immunosorbent assay (ELISA) of spike to fibrinogen ( a ) or fibrin ( b ). K d , dissociation constant. A 450 , absorbance at 450 nm. c , Fibrinogen immunoprecipitation (IP) with spike. d , Spike and fibrinogen immunoreactivity in the lungs at 3 d.p.i. Representative of five Beta-infected WT mice. Scale bar, 300 μm. e , Peptide array of fibrinogen chains Aα, Bβ and γ blotted with spike. The binding signal intensity is shown (white to orange). f , Scanning electron microscopy (SEM) images and quantification of the fibrin clot fibre radius in human plasma with spike. The fibre radius distribution was determined in n  = 25 (plasma) and n  = 28 (plasma with spike) images from three biologically independent experiments (generalized linear mixed-effects model with Holmes multiple correction; Methods ) and the fibre radius proportion (<0.05 µm) was determined from n  = 3 biologically independent experiments (two-sided paired t -test; Methods ). Scale bar, 1 µm. FOV, field of view. g , The turbidity of fibrin polymerization with spike in human plasma. h , Immunoblot (IB) analysis of fibrin degradation by plasmin representative from five (0, 2 and 4 h) or three (1 and 6 h) biologically independent experiments. i , ROS in BMDMs stimulated with fibrin and/or spike. n  = 6 (unstimulated and spike) and n  = 3 (fibrin or fibrin with spike) biologically independent experiments. a.u., arbitrary units. j , Fibrin γC domain and spike-binding epitope γ 364–395 (red). Alanine scanning of γ 377–395 blotted with His–spike. The binding of spike to Ala-substituted peptides is shown. The residues that are required for binding are indicated in yellow. k , Competitive ELISA of 5B8-huFc (5B8 with human IgG1 Fc region) or huIgG1 versus spike for binding to fibrin. n  = 3 biologically independent experiments. l , ROS in BMDMs stimulated with fibrin and/or spike treated with 5B8 or IgG2b. n  = 3 biologically independent experiments. Representative data of n  = 3 ( a – c ) or n  = 4 ( g ) biologically independent experiments. For i and l , statistical analysis was performed using one-way analysis of variance (ANOVA) with Tukey’s multiple-comparison test. Data are mean ± s.e.m. Gel source data are provided in Supplementary Fig. 1 .

Source Data

To identify spike-binding regions in fibrinogen, we generated a custom fibrinogen peptide array of 390 15-mer peptides overlapping by 11 amino acids, spanning the Aα, Bβ and γ chains (Fig. 1e and Supplementary Table 1 ). Hybridization with His-tagged trimeric spike identified three major binding sites in the Bβ and γ fibrinogen chains, namely Bβ 119–129 , which contains cleavage sites for the fibrinolytic serine protease plasmin 25 ; γ 163–181 , of unknown function; and γ 364–395 , which encompasses the γ 377–395 cryptic fibrinogen-binding site for complement receptor 3 that activates innate immune responses 15 , 26 (Fig. 1e ). Mapping the spike-binding peptides onto the fibrinogen crystal structure revealed proximity of the γ 163–181 and γ 377–395 peptides, suggesting that a 3D conformational epitope in the carboxy-terminal γ-chain of fibrinogen (γC domain) is involved in fibrinogen binding to spike (Extended Data Fig. 1e ). Reverse mapping of fibrinogen binding on SARS-CoV-2 spike variants revealed binding sites spike 37–103 , spike 229–251 within the N terminal domain (NTD) S1 subunit, spike 305–319 , spike 341–355 within the receptor-binding domain (RBD) and spike 1049–1063 within the S2 subunit (Extended Data Fig. 1f and Supplementary Table 2 ). Computational docking identified a model with the best docking energies with close association between fibrinogen γ 364–395 and spike 37–103 (Extended Data Fig. 2 and Supplementary Table 3 ).

We next tested whether spike interferes with the polymerization, degradation and inflammatory properties of fibrin. Incubation of spike with healthy donor plasma in the presence of thrombin, which is elevated during COVID-19 1 , resulted in altered clot structure shown by scanning electron microscopy (SEM) and increased turbidity of fibrin clot formation (Fig. 1f,g and Extended Data Fig. 3a–c ). Incubation of spike with fibrin delayed plasmin degradation of both the β-chain and the γ–γ dimer (Fig. 1h ), suggesting that spike delays fibrinolysis. These findings are consistent with the formation of dense fibrin clots with thin fibres in thromboembolic diseases and fibrinolysis-resistant blood clots in patients with COVID-19 1 , 13 , 23 . Notably, spike increased fibrin-induced release of reactive oxygen species (ROS) in a concentration-dependent manner in bone-marrow-derived macrophages (BMDMs), while spike alone did not have an effect (Fig. 1i ), suggesting that the SARS-CoV-2 virus enhances fibrin-induced inflammation. Using alanine scanning mutagenesis, we found that spike interacts with amino acids 386–394 in the C terminus of the γ 377–395 peptide (Fig. 1j and Extended Data Fig. 3d )—the main site for binding of the CD11b i-domain to fibrin 26 . Blockade of the fibrin amino acids 386–394 epitope with 5B8, a therapeutic mouse monoclonal antibody against the fibrin γ 377–395 peptide 17 , inhibited the interaction between human fibrin and spike (Fig. 1k and Extended Data Fig. 3e ) and suppressed spike-enhanced fibrin-induced ROS release from BMDMs (Fig. 1l ). Inhibition of the fibrin–spike interaction may be influenced by the higher affinity of fibrin for 5B8 (26 nM) 17 than for spike (390 nM), as well as additional binding sites between spike and fibrin. Stereotactic injection of fibrinogen and spike in the mouse brain increased fibrin-induced microglial reactivity (Extended Data Fig. 3f ). Overall, these results reveal a role for fibrinogen as a SARS-CoV-2 spike-binding protein accelerating the formation of abnormal clots with increased inflammatory activity.

Fibrin drives inflammation

Conversion of fibrinogen to fibrin exposes its cryptic inflammatory γ 377–395 epitope 26 . Genetic or pharmacological targeting of this epitope has potent therapeutic effects in autoimmune and inflammatory diseases 15 , 17 , 18 , 19 , 20 , 21 . WT, fibrinogen-deficient ( Fga –/– ) and Fgg γ390–396A mice, which express mutant fibrinogen that retains normal clotting function but lacks the γ 390–396 motif for binding to the receptor CD11b–CD18, were infected intranasally with a SARS-CoV-2 Beta variant that is naturally mouse adapted (Fig. 2a ). In WT mice, infection induced macrophage infiltration and alveolar haemorrhage, and these were reduced in Fga –/– and Fgg γ390–396A mice (Fig. 2b and Extended Data Fig. 4a,b ). Fibrin induces oxidative stress through CD11b–CD18-mediated activation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 17 , 20 , 21 , which is linked to severe disease and thrombotic events in patients with COVID-19 27 . Fga –/– and Fgg γ390–396A mice had significantly less gp91 phox NADPH oxidase subunit and less of the oxidative stress marker 4-hydroxynonenal in the lungs after infection than did the control mice (Fig. 2b and Extended Data Fig. 4b,c ). Collagen deposition in severe COVID-19 cases is linked to progressive fibrotic lung disease 28 . Collagen accumulation was significantly reduced in the lungs of infected Fga –/– mice and Fgg γ390–396A mice compared with WT (Fig. 2b ). Fibrin deposits were absent in infected Fga –/– mice, as expected, and decreased in Fgg γ390–396A mice (Fig. 2b ). Overall, these results suggest that fibrin signalling through CD11b–CD18 induces inflammatory cell infiltration, oxidative stress and fibrosis in SARS-CoV-2 infection, with implications for long-term complications seen in COVID-19.

a , Lung pathology of Beta-infected WT, Fga −/− and Fgg γ390–396A mice. b , Microscopy analysis of Mac2 (macrophages) and fibrin/fibrinogen in uninfected (UI) ( n  = 4) and Beta-infected WT ( n  =10), Fga −/− ( n  = 10) and Fgg γ390–396A ( n  = 9) mice; gp91 phox in uninfected ( n  = 3) and Beta-infected WT ( n  = 10), Fga −/− ( n  = 10) and Fgg γ390–396A ( n  = 9) mice; and Trichrome (collagen, blue; fibrin, red) in uninfected ( n  = 4) and Beta-infected WT ( n  = 5), Fga −/− ( n  = 5), Fgg γ390–396A ( n  = 4) mice. Data are from mice infected in two independent experiments. c , Gene set enrichment analysis (GSEA) of pathways significantly altered in Beta-infected lungs of Fga −/− mice compared with WT mice. NES, normalized enrichment score. d , Significant genes and pathways. Uninfected: n  = 4 (WT) and n  = 3 ( Fga −/− ) mice; Beta: n  = 4 (WT) and n  = 5 ( Fga –/– ) mice. e , Microscopy analysis of NKp46, granzyme and spike in lung after infection. NKp46: uninfected, n  = 8 (WT); infected, n  = 10 (WT), n  = 10 ( Fga −/− ) and n  = 9 ( Fgg γ390–396A ) mice; granzyme: uninfected, n  = 4 (WT); infected, n  = 5 mice per group; spike: uninfected, n  = 4 (WT); infected: n  = 10 (WT), n  = 10 ( Fga −/− ) and n  = 9 ( Fgg γ390–396A ) mice. Statistical analysis was performed using one-way ANOVA with Tukey’s multiple-comparison test ( b and e ) and two-sided quasi-likelihood F -test implemented in edgeR ( d ). In d , bold font indicates adjusted P  < 0.05 (Benjamini–Hochberg). Each lane represents the average scaled z -score for each genotype. Data are mean ± s.e.m. Scale bars, 100 μm ( b and e ). The diagram in a was created with BioRender.

We next assessed the effects of fibrinogen on the lung transcriptome after COVID-19. Fibrinogen deficiency reduced the expression of genes of inflammatory pathways, such as SARS coronavirus and innate immunity ( Ifit2 , Ifit3b , Irf5 , Myd88 , Cxcl10 , Tnfsf9 , Il1rn  and Lif ); regulation of type I interferon (IFN) signalling ( Ifit2 , Ifit3b  and Irf5 ); and the JAK–STAT pathway and NF-κB pathway (Fig. 2c,d and Supplementary Tables 4 and 5 ). The type I IFN response is elevated during active infection and persists as a biomarker for long COVID 7 , 14 . Overlay of gene expression data with the human type I IFN induction and signalling during SARS-CoV-2 infection pathway showed a 73% reduction in type I IFN-regulating genes in infected Fga −/− mice (Extended Data Fig. 5 ). Indeed, expression of the type I IFN-induced gene Cxcl10 , which encodes a key inflammatory cell chemoattractant that is induced by fibrin and is associated with cytokine storm and severe COVID-19 21 , 29 , was markedly reduced in infected Fga −/ − lungs compared with in the controls (Fig. 2d ). By contrast, expression of the natural killer (NK) cell-expressed surface antigen-encoding genes Klrb1a and Klra9 and the cytotoxic gene Prf1 was increased in infected Fga −/ − mice compared with infected controls (Fig. 2d ). Accordingly, NK1.1 expressed in NK cells, NKT cells and ILC1 cells, as well as NKp46 and granzyme, were upregulated in the lungs of infected Fga −/− and Fgg γ390–396A mice (Fig. 2e and Extended Data Fig. 6a ), suggesting a role for fibrin as a regulator of NK cells in infection.

Reduced NK cell recruitment and activation impairs virus elimination and has been linked to poor outcomes in COVID-19 30 . Spike and N proteins were reduced in Fga −/ − and Fgg γ390–396A mice compared with WT mice (Fig. 2e and Extended Data Fig. 6b ). In plaque-forming assays, virus levels were reduced in lung lysates of Fga −/− mice (Extended Data Fig. 6c ), suggesting that the effects of fibrinogen could be attributed to regulation of immune pathways or lower levels of the virus. Although there was a trend for reduced viral titres in lung lysates of  Fgg γ390–396A , the titres were too variable to be statistically significant (Extended Data Fig. 6c ). A robust increase in NK cells coupled with decreased viral production in the lungs after fibrin depletion or inhibition of fibrin’s interaction with the receptor CD11b–CD18 during SARS-CoV-2 infection is consistent with increased activation of CD11b-deficient NK cells during tumour surveillance 31 .

Fibrin suppresses NK cells

To determine the mechanism of fibrin-induced NK cell suppression, we first performed bulk RNA-sequencing (RNA-seq) analysis of fibrin-stimulated primary mouse NK cells, and identified 277 downregulated genes and 76 upregulated genes (Extended Data Fig. 7a and Supplementary Table 6 ). Fibrin suppressed genes encoding molecules that control NK cell-mediated immunity ( Gzmb , Gzmc  and Crtam ), cytokines and chemokines ( Ccl3 , Ifng  and Csf2 ), the response to ROS ( Hmox1 , Prdx1  and Selenos ), IL-2 signalling ( Bhlhe40 , Cst7  and Il2ra ), NF-κB signalling ( Ccl4 , Nr4a3  and Tnfrsf9 ) and translation ( Eif4ebp1 , Mrpl17  and Mrpl23 ) (Fig. 3a and Supplementary Table 6 ). Fibrin markedly suppressed a network of pathways, including mitochondrial function, leukocyte migration, cytokine/chemokine production, inflammatory response, proliferation and MAPK (Fig. 3b and Supplementary Table 7 ). Using quantitative mass spectrometry (MS) phosphoproteomics and kinase activity analysis 32 , we globally characterized the dynamics of protein phosphorylation and kinase–substrate relationships in human NK cells in response to fibrin or IL-15 (Fig. 3c and Supplementary Tables 8 – 10 ). Fibrin downregulated the JAK–STAT pathway compared with IL-15, as well as multiple targets of the p38 MAP kinase (that is, MAP2K3, MAP2K6, MAPKAPK3, MAPKAPK5 and RAF1), consistent with the role of these pathways in regulating NK cell activation 33 (Fig. 3c and Extended Data Fig. 7b ). Phosphoproteomic network analysis revealed that, compared with IL-15, fibrin reduced the induction of JAK–STAT5, MTOR–S6K (also known as RPS6KB1) and LCK pathways (Extended Data Fig. 7c ), which are essential for the effector functions, energy metabolism and survival of NK cells in COVID-19 33 . Furthermore, fibrin reduced surface expression of NK cell activation markers (NKp46, NKG2d, CD54), cell proliferation and production of IFNγ and granzyme B (Extended Data Fig. 7d–f ). In contrast to its effects in primary macrophages and microglia 21 , in NK cells, fibrin suppressed cytokine activities, IFN response, inflammation, MAPK signalling, proliferation, response to lipid, viral process and NF-κB signalling (Fig. 3d ). Indeed, comparison of kinase signalling responses between fibrin-treated NK cells (this study) and fibrin-treated macrophages 21 revealed that fibrin differentially regulated signalling pathways in the two cell types (Extended Data Fig. 7g ).

a , Heat map of selected genes and pathways from bulk RNA-seq analysis of primary mouse NK cells stimulated with fibrin for 4 days in vitro. n  = 3 mice. Each lane represents the normalized scaled expression ( z score) from each individual mouse ( Methods ). b , Fibrin-suppressed GO term networks from bulk RNA-seq analysis of primary mouse NK cells. Each circle represents one significantly altered pathway. NES, normalized enrichment score.  c , Kinase activities inferred as a z score of phosphorylated substrates from global MS phosphoproteomics analysis of NK cells isolated from PBMCs unstimulated (mock) or treated with fibrin or IL-15 for 1 h. The colours indicate an increase (red) or decrease (blue) in kinase activity. The black bounding boxes indicate a significant shift in kinase-specific substrate regulation. Statistical analysis was performed using a two-tailed z -test (unadjusted P  < 0.05) based on the log 2 -transformed fold changes between n  = 8,054 phosphorylation sites derived from 2 (mock), 3 (fibrin) and 2 (IL-15) biologically independent experiments ( Methods ). d , The NES of selected pathways from GSEA of fibrin-induced genes in NK cells (shown in b ) and macrophages (mac.; scRNA-seq data from a previous study 21 ). e , Microscopy analysis of Mac2 and spike in the lungs of Beta-infected WT, Fga –/– and Fgg γ390–396A mice given intraperitoneal injection of anti-NK1.1 or IgG2a at a dose of 8 mg per kg body weight. Nuclei were stained with DAPI (blue). Scale bars, 50 μm (Mac2) and 200 μm (spike). Uninfected: n  = 4 WT mice; Beta infected: n  = 5 mice per group. Statistical analysis was performed using two-way ANOVA with Tukey’s multiple-comparison test. Data are mean ± s.e.m.

We next tested whether the pathogenic effects of fibrinogen in COVID-19 depend on its inhibitor effects on NK cells. We infected WT, Fga −/− and Fgg γ390–396A mice with SARS-CoV-2 Beta after NK cell depletion with anti-NK1.1 antibody (Supplementary Table 11 ). Depletion of NK1.1 + cells abolished the protection provided by fibrinogen depletion indicated by increased macrophages, oxidative stress, N protein and spike in Fga −/− and Fgg γ390–396A lungs to WT levels (Fig. 3e and Extended Data Fig. 8 ). These findings indicate that fibrinogen is required for SARS-CoV-2 infection in the lung and pulmonary lesion formation through inflammatory activation and suppression of viral clearance involving NK cells.

Infection-independent fibrin functions

Persistent circulating SARS-CoV-2 spike has been reported in long COVID 34 . We hypothesized that the interplay between fibrin and spike might regulate thromboinflammation in COVID-19 beyond active infection. We generated HIV virions pseudotyped with trimeric spike (spike PVs) that are unable to engage mouse ACE2 receptors (Extended Data Fig. 9a,b ). Similar to recombinant spike (Fig. 1 ), spike PVs co-immunoprecipitated with fibrinogen and increased fibrin-induced oxidative stress in BMDMs (Extended Data Fig. 9c,d ). Spike PVs given by i.v. injection into WT mice induced extensive fibrin deposition in the lungs (Fig. 4a and Extended Data Fig. 9e,f ). In WT mice, spike PVs activated macrophages and increased expression of gp91 phox in the lungs, indicating oxidative stress (Extended Data Fig. 9g ). By contrast, control bald PVs or PVs expressing the Env protein from the HIV-1 (HIV-1 PVs) did not induce these effects (Extended Data Fig. 9g ), suggesting that lung pathology was specific for spike. Fga −/− and Fgg γ390–396A mice had reduced macrophage activation and oxidative stress in the lungs after spike PV administration (Fig. 4b,c and Extended Data Fig. 9h ). In a mouse model of fibrinogen-induced encephalomyelitis 35 , co-injection of spike PVs increased fibrin-induced microglial reactivity (Extended Data Fig. 9i ), suggesting that spike enhances the inflammatory function of fibrin in vivo. These results suggest a fibrin-dependent mechanism that elicits inflammatory and oxidative stress responses in the presence of spike in the absence of active infection, which could therefore have a role in long COVID. Notably, we do not believe that this mechanism is related to the rare clotting complications observed with adenovirus based COVID vaccines because the production of anti-PF4 autoantibodies and ensuing drop in platelet counts are triggered by the vector rather than spike 36 . In general, COVID-19 RNA vaccines lead to small amounts of spike protein accumulating locally and within draining lymph nodes where the immune response is initiated and the protein is eliminated 37 . Consistent with the safety of the spike mRNA vaccines, mRNA vaccines prevent post-COVID-19 thromboembolic complications 38 and a cohort study in 99 million COVID-vaccinated individuals showed no safety signals for haematological conditions 39 .

a , Lung pathology from spike PV i.v. administration in WT, Fga −/− and Fgg γ390–396A mice. The diagram was created using BioRender. b , c Mac2 and gp91 phox microscopy and quantification in lungs of WT, Fga –/– and Fgg γ390–396A mice after bald or spike PV administration. n  = 6 mice per group. Statistical analysis was performed using two-tailed Welch two-sample t -tests followed by multiple-correction testing using the Holm procedure. Data are mean ± s.e.m. Scale bars, 50 µm ( b and c ).

Fibrin-targeting antibody in SARS-CoV-2

Neutralizing fibrin toxicity is an attractive therapeutic strategy for neuroprotection and selective suppression of pathogenic inflammation 17 , 40 . The monoclonal antibody 5B8 targeting the fibrin inflammatory domain γ 377–395 provides protection from autoimmune- and amyloid-driven neurodegeneration without adverse effects on haemostasis 17 . We tested the effects of 5B8 after i.n. infection with two different variants of SARS-CoV-2 in models with and without neuroinvasion, as well as in the spike PV non-infectious model (Supplementary Tables 12 and 13 ). Mice were infected with 10 4 plaque-forming units (PFU) and 10 3 PFU for 3 and 7 days post infection (d.p.i.), respectively, for optimal survival and pathological alterations. In WT mice infected with SARS-CoV-2 Beta, prophylactic administration of 5B8 reduced macrophage activation, oxidative stress, collagen accumulation, fibrin deposition and viral spike and N protein expression, while increasing NK cell responses in the lungs compared with the isotype IgG2b-treated controls (Fig. 5a,b and Extended Data Fig. 10a ). No differences in the viral titres in the lung lysates were observed, potentially due to titre variability (Extended Data Fig. 10b ). Therapeutic 5B8 administration 24 h after infection decreased macrophage activation and oxidative stress assessed at 7 d.p.i. (Fig. 5c and Extended Data Fig. 10c ). 5B8 spatially correlated with fibrin-rich areas in the brain of Beta-infected WT mice (Extended Data Fig. 10d ), demonstrating target engagement. These findings suggest that fibrin-targeting immunotherapy suppresses SARS-CoV-2 pathogenesis.

a , Beta infection of 5B8-treated WT mice. b , c , Lung pathology in WT mice prophylactically treated with 5B8 or IgG2b ( n  = 5 (Trichrome, N protein); n  = 10 (Mac2, gp91 phox , spike, granzyme)) at 3 d.p.i. ( b ) or therapeutically treated with 5B8 ( n = 11) or IgG2b ( n = 12) (Mac2 and gp91 phox ) at 7 d.p.i. ( c ). d , Beta infection of WT, Fg a −/− and Fgg γ390–396A mice or 5B8-treated WT mice at 7 d.p.i. e , Fibrinogen and IBA1 in the cortex, representative of four Beta-infected WT mice. f , IBA1 in the hippocampus. UI: n  = 6 mice; Beta infected, prophylactic: n  = 10 (prophylactic 5B8 or IgG2b) mice per group; Beta infected, therapeutic: n  = 12 (IgG2b) and n  = 11 (5B8) mice. g , IBA1 and CD68 in the hippocampus. Uninfected: n  = 6 WT mice; Beta infected, n  = 6 (WT), n  = 6 ( Fga −/− ) or n  = 5 ( Fgg γ390–396A ) mice. h , Delta infection of 5B8-treated K18-hACE2 mice. i , Fibrinogen and IBA1 in various brain regions of uninfected and Delta-infected mice at 3 d.p.i. Uninfected: n  = 4 (hippocampus (Hippo)) and n  = 5 (corpus callosum (Cc), striatum (Str) and frontal cortex (FCtx)) mice; Delta infected: n  = 4 (frontal cortex) and n  = 5 (hippocampus, corpus callosum, striatum) mice. j , k , IBA1, CD68, calbindin and NeuN in the cortex ( j ) and hippocampus ( k ). Uninfected: n  = 5 mice; Delta infected, prophylactic, 3 d.p.i.: n  = 5 (IgG2b) or n  = 4 (5B8) mice; Delta infected, therapeutic, 9 d.p.i.: n  = 6 mice per group. l , Mouse survival and weight. n  = 12 mice per group (therapeutic, 5B8 or IgG2b, Delta infected). Statistical analysis was performed using log-rank tests (survival) and a mixed-effects model (weight). m , Significantly altered genes in the hippocampus of Delta-infected mice given 5B8 or IgG2b. n  = 6 mice per group. Statistical analysis was performed using two-sided unpaired t -tests (unadjusted P  < 0.05; Methods ). For a – f and h – m , 5B8 or IgG2b was given intraperitoneally at a dose of 30 mg per kg body weight, prophylactically (at 0 d.p.i.) or therapeutically (at 1 d.p.i.). Statistical analysis was performed using two-tailed Mann–Whitney U -tests ( b (all except for granzyme) and c ), two-tailed Welch t -tests with Holm multiple-comparison correction ( b (granzyme) and i ) and one-way ANOVA Tukey’s multiple-comparison test ( f , g , j and k ). Data are mean ± s.e.m. Scale bars, 100 μm ( b , c , e , j and i ) or 50 μm ( f , g and k ). The diagrams in a , d and h were created using BioRender.

COVID-19 neuropathology is characterized by microglial reactivity and fibrin deposition, grey matter decrease, microhaemorrhages and small infarcts, and myelin alterations 2 , 3 , 8 , 9 . COVID-19 neurological symptoms and neuropathological alterations have been attributed to secondary effects of systemic SARS-CoV-2 infection, such as cytokine storm and thrombotic complications, or to direct viral infection of the brain 2 , 7 , 14 , 41 . We first tested the role of fibrin in the absence of brain infection using a SARS-CoV-2 Beta mouse-proficient variant that is not associated with neuroinvasion 42 (Fig. 5d ). In Beta-infected C57BL/6 mice, we found fibrin deposits in the brain at sites of microglial reactivity at 7 d.p.i. (Fig. 5e ), reminiscent of neuropathologic alterations observed in patients with COVID-19, as well as infected hamsters or mice 8 , 9 , 43 , 44 . Prophylactic or therapeutic administration of 5B8 decreased microglial reactivity in the hippocampus after Beta infection compared with the IgG2b isotype control (Fig. 5f ). Accordingly, Fga −/− and Fgg γ390–396A mice had reduced microglial reactivity in the hippocampus after SARS-CoV-2 Beta infection (Fig. 5g ), suggesting that fibrin promotes neuroinflammation in COVID-19.

We next tested the effects of fibrin immunotherapy on the K18-hACE2 mouse model of neuroinvasion using the Delta SARS-CoV-2 variant (B.1.617.2), which has been associated with risk of long COVID 45 , 46 (Fig. 5h ). Brains from intranasally infected K18-hACE2 mice had extensive microgliosis as reported previously 45 , associated with upregulation of disease-associated microglial markers (Fig. 5i and Extended Data Fig. 11 ). We found fibrin deposits at sites of microglial reactivity and decreased myelin intensity in the frontal cortex and rostral migratory stream area (Fig. 5i and Extended Data Fig. 12a ). No differences in viral titres in the lung lysates were observed potentially due to titre variability (Extended Data Fig. 12b ). Prophylactic 5B8 administration decreased microglial reactivity and white-matter injury compared with the IgG2b isotype control (Fig. 5j and Extended Data Fig. 12c,d ). In infected mice, 5B8 reduced the loss of cortical neurons or calbindin-expressing interneurons (Fig. 5j ), a feature of severe COVID-19 brain pathology associated with microglial nodules and neurovascular injury 9 . Therapeutic treatment with 5B8 at 1 d.p.i. inhibited microglial reactivity and increased neuronal survival (Fig. 5k ). Therapeutic 5B8 also improved the survival rates with concomitant effects on weight loss (Fig. 5l ). As K18-hACE2 mice had to be euthanized after reaching the humane end points 47 , the effects on survival could not be assessed past day 9. Transcriptomic analysis of the brains of Delta-infected K18-hACE2 mice showed that 5B8 suppressed genes encoding proinflammatory cytokines/chemokines ( Il17c , Ifna2 , Il22 , Il16 , Cxcl10 , Ccl12  and Ccl17 ), IFN-induced genes ( Ifit1 , Ifit3 , Ifi44 , Irf7  and Ifitm3 ), and genes encoding receptors ( Tlr7 , Il6ra , Il17rc ) and coagulation factors ( Plat  and Plg ) while increasing the expression of homeostatic genes ( Cx3cr1 , Irf3  and Hpgd ) (Fig. 5m and Supplementary Table 14 ). Gene Ontology network analysis revealed that 5B8 downregulated pathways related to proliferation, IL-6 signalling, chemotaxis and response to type I IFNs (Extended Data Fig. 12e and Supplementary Table 15 ), consistent with human multiomic profiling of brains of individuals with COVID-19 7 . Finally, 5B8 reduced inflammation and oxidative stress in spike PV-injected mice (Extended Data Fig. 12f ), suggesting that neutralizing fibrin could have a protective effect during persistent presence of spike beyond the active infection. Thus, fibrin-targeting immunotherapy provides protection from pulmonary pathology, neuroinflammation and neurodegeneration in COVID-19.

Although clotting complications in COVID-19 have previously been attributed primarily to systemic inflammation 14 , our findings suggest that coagulopathy in COVID-19 is not merely a consequence of inflammation, but rather serves as an apical driver of infection-induced thromboinflammation and neuropathology. Our data reveal a causal immunomodulatory role for fibrinogen in thromboinflammation and neuropathology in COVID-19. Our findings suggest that fibrin promotes neuropathological alterations either indirectly by inducing hyperinflammation through modulation of NK cells and macrophages in the infected lung or directly on microglia, owing to its parenchymal deposition in the brain after extravasation through a leaky BBB. Indeed, fibrin was sufficient to induce heightened microglia reaction in the presence of spike in the brain even in the absence of peripheral infection. Furthermore, fibrin blockade abolished neuropathology in COVID-19 animal models regardless of neuroinvasion. The finding that 5B8 anti-fibrin antibody blocks many of the pathological effects of fibrin in infected animals raises the possibility for therapeutic intervention in this thromboinflammatory pathway. This mechanism might perpetuate the hypercoagulable and proinflammatory state at sites of microvascular injury, as has been reported in patients with acute infection and long COVID 1 , 4 .

We show that fibrin has an immunomodulatory role promoting increased viral load and thromboinflammation in COVID-19. NK cell recruitment and activation are modulated by extracellular stimuli and interactions with monocytes and dendritic cells 33 . Through genetic loss-of-function studies, multiomics and functional assays on primary mouse and human NK cells, we show that fibrin suppresses transcriptomic and phosphoproteomic signal transduction pathways controlling NK cell cytotoxicity, proliferation and migration. Our in vitro experiments in purified NK cells stimulated with IL-15 suggest that fibrin interferes with IL-15 signalling. Thus, the effects of fibrin in vivo could be due to either regulation of IL-15 signal transduction or by limiting IFN and downstream IL-15 levels. These findings support a model in which coagulopathy functions as an extrinsic signal that may negatively regulate NK cell effector functions or recruitment through fibrin deposition. A procoagulant state leading to fibrin deposition in tissues may be particularly relevant to the impaired clearance of viral infections, where misdirected NK cells and activated macrophages contribute to disease severity. The fibrin-induced suppression of NK cells that we observed is consistent with enhanced cancer cell survival in vitro after co-culture with fibrin-stimulated NK cells 48 , suggesting a role for fibrin in other diseases with vascular damage and impaired NK cell cytotoxicity, such as cancer and autoimmune diseases 49 .

Increased BBB permeability associated with parenchymal fibrin deposition is a feature of COVID-19 neuropathology 8 , 9 . In the brain of some patients with COVID-19, detection of spike and viral RNA suggests potential neuroinvasion 41 , 45 . Our data and previous literature support that, while spike can enhance fibrin toxicity, even in the absence of spike, fibrin is deleterious in diseases such as multiple sclerosis, Alzheimer’s disease, rheumatoid arthritis, colitis and periodonditis 15 , 18 , 19 , 20 . Thus, fibrin may be deposited either together with spike when spike is present in the brain 45 or through an open BBB after peripheral infection without neuroinvasion or spike coupling. Accordingly, the in vivo efficacy of 5B8 could depend on the inhibition of fibrin binding to spike (this study) or to its anti-inflammatory properties in brain and periphery at sites of fibrin deposition 17 , 40 , suggesting a dual mechanism of action for the fibrin immunotherapy in COVID-19. Given the hypercoagulable state in patients with COVID-19 with brain fog and the role of elevated plasma fibrinogen in increasing BBB permeability in mice, high plasma fibrinogen levels in COVID-19 may contribute to BBB disruption and ensuing neuropathology 5 , 50 . Importantly, we show that targeting fibrin is neuroprotective regardless of animal model, viral strain or neuroinvasion, suggesting a global deleterious role for fibrin in COVID-19 neuropathology.

Our study has several limitations. While we used quantification of myelin basic protein (MBP) intensity and the percentage of MBP + area to demonstrate decreased myelin, future studies using electron microscopy will be required to measure demyelination. As the physiological spike concentration in the brain is not fully known, dose–response studies would be required to identify the lowest spike concentration that can enhance fibrin-induced neuroinflammation. In addition to lung, other tissues, such as heart, gut and brain, can be analysed after administration of spike PVs to test the role of fibrin in non-infectious animal models. We performed proof-of-principle in vivo studies to test the efficacy of 5B8 in three animal models of COVID-19. Further preclinical pharmacology will be necessary to evaluate the therapeutic window, dose–response of antibody and viral titres, role of mouse age and genetic background and therapeutic effects in additional species. Given the heterogenous patterns of COVID-19 neuropathology influenced by disease severity and viral strain 44 , the thromboinflammation mechanism that we described represents only one of the pleiotropic mechanisms of neuroinflammation within the spectrum of COVID-19.

Together, data from pathology, radiology and serology in patients with acute COVID-19 and long COVID 1 , 5 , 6 , 8 , 9 , 10 , 22 , 23 , as well as the genetic loss-of-function, pharmacological and transcriptomic studies in three animal models of COVID-19 (this study), establish fibrin as a key driver of inflammation and neuropathology in SARS-CoV-2 infection. Fibrin immunotherapy may represent a strategy for reducing systemic thromboinflammation and neurological manifestations of COVID-19 in both acute and long COVID. Compounded by cumulative risk of memory impairment and cognitive disorders due to breakthrough COVID-19, additional strategies are needed to provide protection against the long-term disease burden 4 . Fibrin immunotherapy may protect from cognitive symptoms associated with COVID-19, as genetic elimination of the fibrin inflammatory epitope protects Alzheimer’s disease mice from synapse loss and cognitive impairment 20 . The fibrin inflammatory epitope is not required for fibrin polymerization or platelet aggregation, and in contrast to anticoagulant therapies, it does not increase bleeding risk 15 . Accordingly, 5B8 does not affect normal clotting time in vivo, fibrin polymerization in vitro or activated partial thromboplastin time in human plasma 17 . Thus, fibrin-targeting immunotherapy may represent an approach to selectively suppress COVID-19 pathogenesis in the brain and other organs without adverse effects on normal haemostasis. A humanized affinity-matured derivative of 5B8 has entered phase 1 clinical trials in healthy individuals to assess safety and tolerability 51 . Safety trials will need to be completed for the antibody to qualify for entry into phase 2 trials to assess exploratory clinical end points. As fibrinogen plasma levels in acute COVID-19 are a predictive biomarker for cognitive impairment in long-COVID, it could be used to stratify patients as candidates for entry into phase 2 trials. Fibrin immunotherapy can be tested for its potential to reduce adverse health outcomes due to long COVID as part of a multipronged approach with prevention and vaccination measures.

C57BL/6 mice and K18-hACE2 mice (strain: B6.Cg-Tg(K18-ACE2)2Prlmn/J) were purchased from the Jackson Laboratory. Fga −/− mice 52 and Fgg γ390–396A mice 53 were obtained from J. Degen. Mice were housed under a 12 h–12 h light–dark cycle, 55 ± 5% relative humidity at 20 ± 2 °C with access to standard laboratory chow and water ad libitum. Both male and female mice were used. The mouse ages are indicated for each experimental procedure and were within 3 to 7 months of age. All infection experiments were performed at an AAALAC-accredited ABSL3 facility at Gladstone Institutes. All of the animal procedures were performed under the guidelines set by the Institutional Animal Care and Use Committee at the University of California, San Francisco.

Human plasma and PBMCs

Human citrated plasma (IPLASEATNAC50ML, 1151254) was purchased from Innovative Research. Fresh PBMCs (LP,FR,MNC,2B; 3118730 and 3112992) were purchased from AllCells. All human material used in the study is commercially available and no human participants were recruited.

SARS-CoV-2 recombinant trimeric spike protein production

The plasmid vector pCAGGS containing the SARS-CoV-2,Wuhan‐Hu‐1 ectodomain spike gene with a deletion of the polybasic cleavage site (RRAR to A), two stabilizing mutations (K986P and V987P), a C-terminal thrombin cleavage site, T4 foldon trimerization domain and a hexahistidine tag (6×His) was obtained from BEI Resources (deposited by F. Krammer) 54 . Recombinant spike was produced by transient transfection in CHO cells by Celltheon. Spike was purified by Ni 2+ -NTA affinity chromatography, eluted in phosphate-buffered saline (PBS) containing imidazole, buffer exchanged into 1× PBS and purified by size-exclusion chromatography (Superdex 200 column).

Plasma clot formation assay

Fibrin polymerization in a plasma clot assay was measured by turbidity 17 . In brief, healthy donor citrated human plasma (Innovative Research) was diluted 1:3 in 20 mM HEPES. Recombinant spike was buffer-exchanged into 20 mM HEPES, pH 7.4, 137 mM NaCl (Amicon concentrators, 100 kDa cut-off). Equal volumes (50 µl) of plasma and buffer-exchanged spike were incubated at 25 °C for 15 min. Clotting was initiated by 0.25 U ml −1 thrombin (Sigma-Aldrich) and 20 mM CaCl 2 . The final concentrations were 1:12 plasma, 0.75 μM spike, 0.25 U ml −1 thrombin, 20 mM CaCl 2 . Turbidity was measured at 340 nm every 15 s for 30 min on the SpectraMax M5 microplate reader (Molecular Devices) using SoftMax Pro v.5.2 (Phoenix Technologies).

SEM analysis of fibrin clots

Healthy donor citrated human plasma was diluted 1:3 in 20 mM HEPES buffer, pH 7.4; 15 μl of diluted plasma was mixed with 15 μl of recombinant spike that was buffer-exchanged into 20 mM HEPES and 137 mM NaCl (Amicon concentrators, 100 kDa cut-off) using a low concentration of NaCl to maintain spike solubility and stability. Then, 25 μl of this mixture was pipetted onto 5 mm × 5 mm silicon wafers (Ted Pella) and incubated for 15 min at 37 °C in a humidified tissue culture incubator. Then, 25 µl of a CaCl 2 and thrombin solution in 20 mM HEPES was added in the centre of the wafer and allowed to polymerize at 25 °C for 2 h. The final concentrations were as follows: plasma 1:12, 0.9 μM spike, 0.25 U ml −1 thrombin, 20 mM CaCl 2 . Buffer was used instead of spike for vehicle control. Clots on wafers were placed onto ice, washed twice for 10 min each with ice-cold EM-grade 0.1 M cacodylate buffer, pH 7.4, and fixed with cold EM-grade 2% glutaraldehyde (Electron Microscopy Sciences). The samples were rinsed three times for 5 min in Millipore-filtered, double-distilled water; dehydrated in an ethanol series (20%, 50%, 70%, 90%, 100%, 100% for 2 min each); and critical-point dried with CO 2 . The samples were sputter coated with a thin layer of gold–palladium and imaged on the Zeiss Merlin field-emission SEM at 3.0 keV and a secondary electron detector.

Images at a magnification of ×4,000 were captured across the sample, then were converted to 8-bit using NIH ImageJ (v.1.50). After pixel to μm scaling, each image was cropped into two or three FOVs (8 × 8 μm) using NIH DiameterJ as described previously 55 . The surface plot plug-in in ImageJ generated topographical maps of SEM images. In brief, the best segmentation algorithm was pre-selected based on side-by-side comparison of images before quantification. The Mixed Segmentation (M1 through M3) built in DiameterJ Segment provided the most accurate representation of the fibres to be quantified. The same segmentation method and variant was used across all test conditions and images. Each segmented image was manually edited using ImageJ to ensure complete representation of segmented fibres. The edited images were batch processed using DiameterJ 1-108 (orientation analysis not selected). Fibre radius and intersection densities were collated from each batch. Data from 8–10 FOVs per sample were used for group analysis. Fibre radius distribution in Fig. 1f was calculated using FOVs from all images collected to assess the distribution across the dataset. Fibre radius proportion was statistically analysed based on three biologically independent experiments in Fig. 1f and the quantification and statistical analysis of the individual images from these experiments is shown in Extended Data Fig. 3c . Samples with collapsed fibres due to potential SEM critical-point drying technical artifacts were excluded from further analysis.

For quantification of the fibrin clots by SEM, at each radius, the difference in log-transformed odds ratio of detecting fibres (among all the views in a given image) with the chosen radius under spike versus control conditions was estimated across all images. The log-transformed odds ratio at each radius was estimated using generalized linear mixed-effects models, with the family argument set to binomial and implemented in glmer function in the lme4 (v.1.1-27) package in R 56 , in which the image source for the observations is modelled as a random effect. The P values were corrected for multiple testing using the Holm procedure 57 . In Fig. 1f , the P value represents the significance at each radius across the range of the radii between the two vertical dotted lines. The solid lines represent the best loess fit curves with span parameter set to 0.45.

Fibrinogen- and fibrin-coated ELISA plates

Fibrinogen- and fibrin-coated plates were prepared as described previously 17 . In brief, human plasminogen-free fibrinogen (EMD Millipore) was further diluted to 25 µg ml −1 by adding 20 mM HEPES buffer, pH 7.4 for coating fibrinogen plates or 20 mM HEPES buffer pH 7.4 with 1 U ml −1 thrombin (Sigma-Aldrich) and 7 mM CaCl 2 for fibrin-coated plates. Coating was performed for 1.5 h at 37 °C using 96-well MaxiSorp plates (Thermo Fisher Scientific) and fibrin-coated plates were dried at 37 °C overnight as described previously 17 .

Recombinant SARS-CoV-2 spike protein binding on fibrin or fibrinogen

Fibrin- or fibrinogen-coated 96-well plates were washed with wash buffer (0.05% Tween-20 in PBS), and incubated with blocking buffer consisting of wash buffer with 5% bovine serum albumin (BSA) (Omnipure, Thermo Fisher Scientific) for 1 h at 25 °C. Serial dilutions of recombinant spike or S1(N501Y) were made in binding buffer (wash buffer containing 0.5% BSA). Recombinant spike or S1(N501Y) was added to the wells and incubated for 2 h at 37 °C. After washing five times with wash buffer, rabbit polyclonal anti-6× His tag antibody (ab137839, Abcam, 1:1,000) was added to the plates and incubated for 1 h at 25 °C. After washing, goat anti-rabbit IgG H&L (conjugated with horse radish peroxidase, HRP) (ab205718, Abcam, 1:1000) in wash buffer was added for 1 h at 25 °C. After the final wash, the HRP substrate 3,3′,5,5′-tetramethybenzidine (TMB; Sigma-Aldrich) was added into the wells. The reaction was quenched by adding 1 N hydrochloric acid, and the absorption was measured at 450 nm. Nonlinear regression curves were analysed using Graph Pad Prism 9 software to calculate K d values using a one-site binding model.

Fibrinogen peptide array and spike-binding epitope mapping

A custom PepStar Multiwell Fibrinogen Peptide Array comprising a synthetic peptide library with 390 15-mer peptides representing overlapping peptide scans (15/11) of the α, β and γ fibrinogen chains (UniProt: FIBA, P02671 ; FIBB, P02675 ; FIBG, P02679 ) was generated by JPT Peptide Technologies. The arrays were hybridized with recombinant-His-tagged trimeric spike (1 µg ml −1 in blocking buffer) for 1 h at 30 °C. The His-tag peptide (AGHHHHHH) was also immobilized on the peptide microarray as an assay control. Microarray slides were incubated for 1 h at 30 °C with Alexa 647 anti-6×His monoclonal antibody (MA1-135-A647, Invitrogen) diluted to 1 µg ml −1 in blocking buffer and dried. Before each step, microarrays were washed with washing buffer, 50 mM TBS-buffer including 0.1% Tween-20, pH 7.2. The assay buffer was LowCross buffer (Candor Bioscience). The slides were washed, dried and scanned with a high-resolution laser scanner at 635 nm to obtain fluorescence intensity profiles. The images were quantified to yield a mean pixel value for each peptide. To assess non-specific binding to the peptides and assay performance, a control incubation with secondary antibody was performed in parallel on each slide. The resulting images were analysed and quantified using spot-recognition software (GenePix, Molecular Devices). For each spot, the mean signal intensity was extracted (between 0 and 65,535 arbitrary units). Heat maps were computed and fluorescence intensities were colour-coded. Binding peptides were mapped onto the fibrinogen crystal structure (Protein Data Bank (PDB): 3GHG ) using UCSF Chimera 58 . For the spike peptide array, 1, 0.1 or 0.01 µg ml −1 His-tagged recombinant human fibrinogen γ chain (Novus Bio) was hybridized with the SARS-CoV-2 spike Glycoprotein Variant Collection Peptide Microarray (JPT). Binding was detected using an anti-His secondary antibody conjugated to Alexa 647. Non-specific binding was detected using an anti-His secondary antibody only. Separately, 1, 0.1 or 0.01 µg ml −1 Alexa-647 fibrinogen (Invitrogen) was hybridized onto the spike Glycoprotein Variant Collection Microarray, and peptide binding was directly detected by fluorescence intensity in relative light units (RLU). A heat map was generated by using raw RLU for side-by-side comparison. Spike glycoprotein binding sites on fibrinogen were mapped using the PDB ( 6VSB ).

Peptide alanine scanning

Alanine scanning was performed with custom PepStar Multiwell microarrays (JPT) containing 60 peptides representing Ala substitutions of each residue on fibrinogen peptide γ 377–395 (YSMKKTTMKIIPFNRLTIG). Human full-length IgG and His-tagged peptides were co-immobilized on the microarray slides as controls. His-tagged spike was applied at five concentrations (from 10 μg ml −1 to 0.001 μg ml −1 ) and incubated for 1 h at 30 °C. Two fluorescently labelled secondary antibodies specific to the His tag were added separately for 1 h. Washing and detection was performed as described above and data were analysed with respect to the original peptide. The signal after Ala substitution indicated whether a residue was involved in binding to spike.

Structure preparation and homology modelling

The crystal structure of human fibrinogen (PDB: 3GHG ) was fixed using the Structure Preparation application of the Compute module of MOE. The crystal structure of SARS-CoV-2 spike (PDB: 6VSB ) has missing structural information for flexible loops. To correct these, the Homology Model application in the Protein menu of MOE 2022.02 software (Chemical Computing Group) was used, which includes: (1) initial partial geometry specification; (2) insertions and deletions; (3) loop selection and sidechain packing; and (4) final model selection and refinement. Homology models were inspected using MOE’s Protein Geometry stereochemical quality evaluation tools. The spike crystal structure (PDB: 6VSB ) was prepared by assigning protonation and ionization states.

Docking and calculation of energies of docked complexes

Docking of two proteins was performed by Dock application of Compute module of MOE, using the Protein-Protein function. The application generates a collection of docked configurations from the pool of possible binding positions using the rigid-body docking. To complete a docking procedure, the binding sites were identified based on the peptide array described above. Three potential binding sites were chosen for fibrinogen: (1) 119YLLKDLWQKRQ129 in the β-chain; and, in the γ-chain, (2) 163QSGLYFIKPLKANQQFLVY181 and (3) 364DNGIIWATWKTRWYSMKKTTMKIIPFNRLTIG395. For the ligand (spike protein) five sites were selected. NTD binding region: (1) 37YYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRG103, (2) 229LPIGINITRFQTLLALHRSYLTP251 and (3) 305SFTVEKGIYQTSNF319; RBD region: (4) 341VFNATRFASVYAWNR355; and S2 domain: (5) 1049LMSFPQSAPHGVVFL1063. After receptor, ligand and docking sites were defined, parameters of Dock application of the Compute module of MOE were set to: refinement --Rigid Body, Poses --10. The application created 10 poses, analysed output scores, ligand docking energies and docked poses, and detected the best one; intermediate poses also are saved in a docking database file.

During the docking calculations the program presents 10 best energy complexes. After that, each of the complexes undergone the additional calculations of energy. A computational alanine scan of the fibrinogen molecules in each complex was also conducted with each of the residues in fibrinogen that were experimentally substituted to alanine were computationally substituted to alanine and modelled. The best model was selected on the basis of the lowest docking energy. The computational alanine scan generated the values of correlations between all values of energy for each amino acid substitution and experimental values of the parameter used for estimating the influence of each amino acid. The residues involved in the interaction of this computationally predicted complex were analysed using LigPlot+ v.2.2.

i.v. injection of labelled spike S1(N501Y) and fibrinogen

Spike S1(N501Y) (AcroBiosystems) (20 μg) dissolved in 0.1 M PBS was fluorescently labelled using the Alexa Fluor 647 conjugation kit lightning link (Abcam). The Alexa-Fluor-647-labelled spike S1(N501Y) had a concentration of 1 mg ml −1 . Retro-orbital injections of 0.1 ml of PBS solution containing 20 μg Alexa-647-conjugated spike S1(N501Y) and 30 μg Alexa-546-labelled human fibrinogen (Invitrogen) were performed under isoflurane anaesthesia (1 ml insulin syringe with a 30-gauge needle). The mice were perfused at 1 day after injection with heparinized PBS and fixed with 4% paraformaldehyde (PFA) and lungs were collected for clearing.

3DISCO clearing and light-sheet imaging

3DISCO lung tissue clearing was performed as described previously 59 . Mouse lungs were placed into a 20 ml scintillation glass vial and incubated in 20 ml of THF (Tetrahydrofuran, Roth, CP82.1) gradient in distilled water in a fume hood with gentle shaking at 50% once, 70% once, 80% once and 100% twice for 6 h for each step, followed by 3 h in dichloromethane (DCM, Sigma-Aldrich, 270997). The samples were immersed in BABB solution (benzyl alcohol + benzyl benzoate 1:2 (v/v), Sigma-Aldrich, 24122 and W213802) until optical transparency. Lung tissues were imaged using Imspector Pro v.7.0.98 and the LaVision BioTec Ultramicroscope II light-sheet microscope in a quartz cuvette filled with ethyl cinnamate (ECi) (Sigma-Aldrich). For imaging, MVX10 zoom body (Olympus) with a ×2 objective (pixel size, 3.25 µm for x and y ) at magnification from ×0.63 up to ×1.6 was used. Up to 1,400 images were taken for each lung using a z -step size of 3.5 µm, and light-sheet numerical aperture of 0.111 NA. Band-pass emission filters (mean nm/spread) were used, depending on the excited fluorophores: 525/50 for autofluorescence; 595/40 for AF546; and 680/30 for AF647. The exposure time was 10 ms for single channel and 25 ms for multichannel acquisition. Imaris v.9.5.0 (Bitplane) was used for 3D rendering. Pixel dimensions were updated from the non-reduced 16-bit image metadata. Surface objects in Imaris was used to 3D render focal depositions and spike distribution in representative volumetric ROIs.

Plasmin digestion of fibrin

Before clotting, 3 μM fibrinogen was incubated with 9 μM recombinant spike protein at 37 °C for 1 h in 20 mM HEPES, pH 7.4, 137 mM NaCl, 5 mM CaCl 2 . Thrombin was added at a final concentration of 1.5 U ml −1 . Fibrin clots were allowed to form in Eppendorf tubes for 2 h at 37 °C. Then, 5 μl of 100 μg ml −1 plasmin (Millipore) was added to each tube on top of the clot. All of the samples were incubated at 37 °C for 0, 1, 2, 4 and 6 h; digestion was quenched by adding sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) loading buffer with reducing agent. The samples were heated at 85 °C for 20 min, and aliquots (equivalent to 100 ng fibrinogen) were separated by SDS–PAGE on 4–12% Bis-Tris gels, transferred to PVDF membranes and analysed for anti-human fibrinogen (F4200-06, US Biological, 1:2,000) by western blotting. The band intensities of each protein species (that is, γ–γ dimer, β-chain) were analysed using ImageJ and normalized to the corresponding bands at the 0 h timepoint. The loading control for the western blot is the timepoint 0 before the addition of plasmin to the fibrin clot.

Competitive ELISA of 5B8 versus the spike for binding to fibrin

A 5B8-huFc antibody was synthesized by Fc swap of the mouse IgG2b Fc of 5B8 18 with human IgG1 Fc. 96-well ELISA plates (Greiner) were coated with 25 μg ml −1 IgG-depleted fibrin and incubated in blocking buffer as indicated for binding assays for 1 h before addition of 50 μl per well of 5B8-huFc antibody. Human plasminogen-free fibrinogen was depleted from IgG as described previously 17 . The antibody was diluted at threefold concentrations from 0.0002 μM to 15 μM in PBS with 0.5% BSA and 0.05% Tween-20 (diluent). For the competition ELISA without preincubation, 5B8-huFc was incubated together with 150 nM trimeric spike in diluent (100 μl total volume) for 2 h at 37 °C on fibrin plates. For the ELISA with antibody preincubation, 50 μl of 5B8-huFc was incubated on fibrin plates for 2 h at 37 °C, followed by addition of 50 μl of 150 nM trimeric spike to the antibody and incubation for 2 h at 37 °C. This was followed by incubation with HRP-coupled anti-His tag antibody (MAB050H, R&D Systems, 1:2,000) for 1 h at 25 °C. The ELISA was developed by incubation with TMB/E substrate (Chemicon-Millipore), and the absorbance was measured at 450 nm using the Synergy H4 plate reader (BioTek).

ROS detection

BMDM culture and ROS detection using 5 µM DHE (Invitrogen) were performed as described previously 17 , 60 . In brief, cells were plated on 96-well black μ-clear-bottomed microtitre plates (Greiner Bio-One) precoated with 12.5 µg ml −1 fibrin with or without recombinant spike (0.168, 1.68 and 3.36 µM), spike PVs or bald PVs. For fibrin inhibition, 5B8 or IgG2b (each 20 μg ml −1 ) (MPC-11, BioXCell) was added in fibrin with or without 3.36 µM recombinant spike-coated wells 2 h before plating. Cells were incubated on fibrin for 24 h and DHE fluorescence was detected at 518 nm/605 nm using the SpectraMax M5 microplate reader. As macrophage activation can be influenced by cell culture conditions, heat-inactivated fetal bovine serum and macrophage colony-stimulating factor were batch tested as described previously 60 . As the activity of PVs can be influenced by freeze–thaw cycles, all of the experiments were performed with virions that had been freshly thawed and kept at 37 °C. Refrozen virion samples were not used.

Immunoprecipitation

To test interaction of fibrinogen with His-tagged spike, the Pierce co-immunoprecipitation kit (Thermo Fisher Scientific) protocol was used with original immunoprecipitation/lysis buffer and modifications. Spike and fibrinogen were mixed at a molar ratio of 2:1 in 800 μl of immunoprecipitation buffer (50 mM Tris, pH 8.0, 5% glycerol, 1% NP-40, 100 mM NaCl) supplemented with 100 × EDTA-free Halt protease inhibitor (Thermo Fisher Scientific) and then rotated at 37 °C for 1 h. Resin beads conjugated with the anti-fibrinogen antibody (SAFG-AP, Enzyme Research Laboratories, 1:1,000) were added to the mixture and rotated at 37 °C for another 2 h. The bound proteins were eluted in 60 µl of EB solution and neutralized with 1/10 volume of 1 M Tris, pH 9.0. The wash buffer and EB solution were warmed to 37 °C in advance. The eluted proteins were separated by SDS–PAGE on 4–12% gels, transferred to PVDF membranes (Invitrogen) and incubated with rabbit anti-spike antibody (632604, GeneTex, 1:1,000) and sheep anti-fibrinogen antibody (SAFG-AP, Enzyme Research Laboratories, 1:1,000) and then with HRP-conjugated anti-rabbit (111-035-144, Jackson ImmunoResearch; 1:10000) and anti-sheep (HAF016, R&D Systems; 1:5,000) secondary antibodies. For immunoprecipitation of spike PVs, spike antibodies (GTX635693, GeneTex; 1:1,600) recognizing SARS-CoV-2 spike (S2) were used. For spike PV immunoblot, anti-spike (632604, GeneTex, 1:1,000) and anti-p24 Gag (detecting p55, 1:100) antibodies donated to the Greene laboratory by Beckman Coulter 61 and anti-Vpr (8D1, Cosmo Bio, 1:200) antibodies were used. Protein bands were detected using Immobilon Forte Western HRP substrate (Sigma-Aldrich) and the ChemiDoc imaging system (Bio-Rad).

SARS-CoV-2 culture and in vivo infection

To assess SARS-CoV-2 infection in vivo, viral stocks of SARS-CoV-2 B.1.351 (Beta) and SARS CoV-2 B.1.617.2 (Delta) were prepared on Vero cells expressing transmembrane protease serine 2 (TMPRSS2) and ACE2 (Vero-TMPRSS2-ACE2) 47 provided by A. Creanga and B. Graham at NIH and stored at −80 °C until used. Experiments involving Beta were performed on female and male WT C57BL/6, Fga –/– and Fgg γ390–396A mice (6–7 months of age). The Beta strain contains the K417Y, E484K and N501Y substitutions in the spike RBD and binds to mouse ACE2 inducing active infection in a range of experimental mouse strains 62 , 63 , 64 . Experiments using Delta were performed on female and male 4–5 month old K18-hACE2 mice. For the infection, the animals were anaesthetized using 100 mg per kg ketamine mixed with 10 mg per kg xylazine through intraperitoneal injection. Anaesthetized mice received i.n. administration of an infectious inoculum of virus in 50 μl of serum-free DMEM. For each experiment, lung and brain tissues were collected. Left lung lobes and one brain hemisphere from each animal were placed in 4% PFA for fixation and histological processing. The remaining lung tissue was roughly chopped and processed for homogenates in prefilled zirconium bead tubes (Benchmark Scientific). Homogenates were stored at −80 °C. The remaining brain hemispheres were flash-frozen and stored at −80 °C. All aspects of this study were approved by the office of Environmental Health and Safety at UCSF before initiation. Work with SARS-CoV-2 was performed in a biosafety level 3 laboratory by personnel equipped with powered air-purifying respirators.

Plaque assay

Lung homogenates were assessed for viral concentration by plaque assay. In brief, Vero-TMPRESS2-ACE2 cells were plated onto 12-well plates at a concentration of 2 × 10 5 cells per well. Homogenates were added to the cells in a dilution series of 10 1 , 10 2 , 10 3 , 10 4 , 10 5 and 10 6 in serum-free DMEM. The homogenate dilutions were incubated on the cells for 1 h, and the media in the wells was then overlaid with 2.5% Avicel (Dupont, RC-591). Cells were incubated for 72 h, then the overlay was removed and the cells were fixed in 10% formalin for 1 h, and stained with crystal violet to visualize PFU.

Production of spike PVs

HEK293T cells (3.75 × 10 6 ) were plated in a T175 flask and transfected 24 h later with 90 μg of polyethyleneimine (PEI; Sigma-Aldrich), 30 μg of HIV-1 NL4-3 ∆ Env eGFP (NIH AIDS Reagent Program) or 3.5 μg of pCAGGS SARS-CoV-2 trimeric spike glycoprotein (NR52310, BEI Resources) in a total of 10 ml of Opti-MEM medium (Invitrogen). The next day, the medium was replaced with DMEM10 complete medium, and the cells were incubated at 37 °C in 5% CO 2 for 48 h. The supernatant was then collected, filtered with 0.22 µm Steriflip filters (EMD, Millipore) and ultracentrifuged at 25,000 rpm for 1.5 h at 4 °C. The concentrated supernatant was removed, the pellets (viral particles) were resuspended in cold 1× PBS containing 1% fetal bovine serum and aliquots were stored at −80 °C in a biosafety level 3 laboratory. For the production of control viral particles not expressing the spike glycoprotein (bald), the same procedure was used but with the omission of the pCAGGS SARS-CoV-2 spike vector transfection. HIV Env pseudotyped viral particles were also produced with the same procedure, using an HIV89.6 Env dual tropic (X4 and R5) expression vector (NIH AIDS Reagent Program) instead of the spike expression vector.

In vivo administration of SARS-CoV-2 spike PVs

Mice were anaesthetized with isoflurane and spike PVs or bald PVs (control) (100 µl) were slowly injected into the retro-orbital plexus with a BD 0.3 ml insulin syringe attached to a 29-gauge needle. After 3 min, the needle was slowly withdrawn, and the mice were allowed to recover. As the activity of PVs can be influenced by freeze–thaw cycles, all of the experiments were performed with virions that had been freshly thawed and kept at 37 °C. Refrozen virion samples were not used. SARS-CoV-2 spike PVs were administered to 3- to 4-month-old mice.

5B8 penetration in the CNS and target engagement after SARS-CoV-2 infection

C57BL/6 mice (4–5 months of age) were infected with 10 4 PFU of SARS-CoV-2 B.1.351 (Beta). On 5 and 7 d.p.i, mice were given intraperitoneally 30 mg per kg of the 5B8-huFc antibody. On 7 d.p.i, mice were perfused with saline followed by fixation with 4% PFA. Subsequently, the brains were post-fixed in the same fixative and cryoprotected in 30% sucrose. The brain hemispheres were frozen in OCT and sectioned (10 µm sections). Sagittal brain sections were incubated with 0.1% Sudan Black (dissolved in 70% ethanol) for 10 min, permeabilized/blocked with 3% BSA and 3% NDS in PBS containing 0.1% Triton X-100 for 1 h. The sections were incubated overnight with an antibody to fibrinogen (1:2,000), followed by Alexa Fluor 594 donkey anti-rabbit IgG (1:1,000; Jackson ImmunoResearch) for 1 h. To detect 5B8-huFc antibody in the brain, the sections were stained with F(ab′)2-donkey anti-human IgG (H+L) cross-adsorbed secondary antibody, FITC (ab102424, Abcam, 1:300) for 1 h. The sections were covered with glass coverslips, sealed with ProLong Diamond Antifade Mounting reagent (Thermo Fisher Scientific) and kept at 4 °C until imaging.

Fibrin 5B8 antibody treatment

For prophylactic pharmacological treatment of SARS-CoV-2 B.1.351 (Beta) infection, anti-fibrin antibody 5B8 17 or an isotype-matched IgG2b (MPC-11, BioXCell) control were administered intravenously by retro-orbital injection at 30 mg per kg in 5- to 6-month-old C57BL/6 mice. Then, 1 h later, the mice were given 10 4 PFU of Beta through the i.n. route in a final volume of 50 μl. Beta-infected mice were euthanized at 3 days for histological analysis. For SARS CoV-2 B.1.617.2 (Delta) infection, 4- to 5-month-old K18-hACE2 mice were given 5B8 or IgG2b intravenously through retro-orbital injection at 30 mg per kg 1 h before Delta infection and every 48 h intraperitoneally, and were euthanized at 3 d.p.i. For therapeutic treatments, 5B8 or IgG2b were given intraperitoneally at a dose of 30 mg per kg at 1 d.p.i. with 10 3 PFU of Beta in 5- to 6-month-old C57BL/6 mice or Delta in 4- to 5-month-old K18-hACE2 mice as described above, and every 48 h thereafter, intraperitoneally. The animals were euthanized at 7 or 9 d.p.i. For spike PVs, 5B8 or IgG2b isotype control were given intravenously to C57BL/6 mice by retro-orbital injection at 30 mg per kg 15 min before injection of PVs. Generation of 5B8 and dose of administration have been described previously 17 . Administration of mouse monoclonal antibodies intraperitoneally provides sustained release of antibody into the bloodstream and thus is commonly used to assess preclinical efficacy for antibodies that will eventually be delivered intravenously in the clinic 65 , 66 , 67 .

Histology and immunohistochemistry

Histopathological analysis in mouse lung and brain was performed on frozen or paraffin sections 17 , 68 , 69 . Serial sections were not collected in the study. Lung sections were stained with haematoxylin and eosin and trichrome. The following antibodies were used: rabbit anti-SARS-CoV-2 nucleocapsid (GTX135357, GeneTex, 1:500), mouse anti-SARS-CoV-2 spike (1A9, GeneTex, 1:100), sheep anti-fibrinogen (F4200-06, US Biological, 1:300), rabbit polyclonal anti-fibrinogen (gift from J. Degen, 1:500), rat anti-mouse/human Mac2 (M3/38, Cedarlane, 1:500), mouse anti- gp91 phox (53/gp91-phox, BD Biosciences, 1:500), rat anti-mouse CD335 (NKp46) (29A1.4, BD Biosciences, 1:500), mouse anti-NK1.1 (PK136, Invitrogen, 1:250) and rabbit anti-granzyme A (PA5-119160, Invitrogen, 1:500). Brains were cut with a cryostat into 30-μm-thick frozen sections for free-floating immunostaining. The following antibodies were used: rabbit anti-IBA1 (019-19741, Wako, 1:1,000), rat anti-mouse CD68 (FA-11, BioLegend, 1:500), guinea pig anti-NeuN (A60, Sigma-Aldrich, 1:500), rat anti-myelin basic protein (ab7349, Abcam, 1:100) and rabbit anti-calbindin (CB38a, Swant; 1:5,000). The tissue sections were washed in PBS and incubated in a blocking and permeabilization buffer consisting of PBS supplemented with 0.2% Triton X-100 and 5% BSA for 1 h at 25 °C. For mouse primary antibodies, the sections were incubated in M.O.M. (Mouse on Mouse Immunodetection Kits, Vector Laboratories) mouse IgG blocking reagent diluted in PBS containing 0.2% Triton X-100 and 5% BSA, and then with M.O.M. diluent for 5 min at room temperature. The sections were rinsed twice with PBS containing 0.1% Triton X-100 and incubated overnight with primary antibodies at 4 °C. All of the tissue sections were washed with PBS containing 0.1% Triton X-100 and incubated with the following secondary antibodies: goat anti-rabbit Alexa Fluor 488 (A-11008, Thermo Fisher Scientific, 1:1,000), goat anti-mouse Alexa Fluor 568 (A-110041, Thermo Fisher Scientific, 1:1,000) or goat anti-rat Alexa Fluor 647 (A-21247, Thermo Fisher Scientific, 1:1,000), and stained with DAPI. The sections were mounted on frosted microscopy slides (Thermo Fisher Scientific), covered with glass coverslips, sealed with ProLong Diamond Antifade Mounting reagent (Thermo Fisher Scientific) and kept at 4 °C until imaging.

Confocal microscopy

Tissue sections were imaged using a laser-scanning confocal microscope FLUOVIEW FV3000RS “Snow Leopard” (Olympus) or Fluoview FV1000 (Olympus), a 40 × and 0.8 NA water-immersion lens or 60× oil-immersion UPLSAPO objective (NA = 1.35) and FV31S-SW software v.2.3.2.169 (Olympus). Individual channels were captured sequentially with a 405 nm laser and a 430/70 spectral detector for DAPI, a 488 nm laser and a 500/40 spectral detector for Alexa Fluor 488, a 561 nm laser and a 570/620 high-sensitivity detector for Alexa Fluor 568, and a 650-nm laser and a 650/750 high-sensitivity detector (Olympus TruSpectral detector technology) for Alexa Fluor 647. Captured images were processed with Fiji v.2.1.0/ImageJ v.1.53c.

Image analysis

To analyse microglia after stereotaxic injections of fibrinogen, spike or PVs, the corpus callosum within five rostrocaudally spaced coronal brain sections was selected for quantification 17 . To quantify IBA1, CD68, calbindin or NeuN + cells in mice infected with Beta or Delta, three areas in the hippocampus (for IBA1 or CD68) or two areas in the cortex (for calbindin or NeuN) were selected on three mediolaterally spaced sagittal brain sections, ensuring consistency in anatomical regions per mouse. For lung pathology in Beta-infected mice, six or seven representative areas were chosen from three lung sections. N protein-positive areas were selected for collagen quantification. Lung pathology in mice injected with PVs was performed on five representative areas selected from three lung sections. Immunostained cells were counted with Jupyter Notebook in Python 3. In brief, an arbitrary threshold was manually set and used for all images in the dataset. The total number of cells per image was estimated using the function peak_local_max from the open-source skimage Python image-processing library, which returns the coordinates and number of local peaks in an image ( https://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.peak_local_max ). Fibrinogen immunoreactivity was quantified using Fiji (ImageJ) as described previously 70 . Python image processing was used to colocalize fibrinogen and spike protein in lung tissues. In brief, a Jupyter Notebook was written to estimate the amount of fluorescence signal overlap between spike and fibrinogen in lung tissues. The Ostu filter from the skimage Python image-processing library was used to threshold each image labelled with spike and fibrinogen ( https://scikit-image.org/docs/0.13.x/api/skimage.filters.html#skimage.filters.threshold_otsu ). After thresholding, each set of images was compared, and pixels were compartmentalized into 4 categories: spike and fibrinogen overlap, spike signal only, fibrinogen signal only and no signal. In each image, the total number of pixels in an image and the number of pixels with signal for spike only, fibrinogen only or both were computed. Correlations were calculated using FOVs from all images collected as indicated in Extended Data Figs. 1b,c and 9f to assess the distribution across the dataset. All images selected for the figures are representative of the quantification of immunostaining for each experimental group.

Bulk RNA-seq

Lungs (3 d.p.i.) were isolated and snap-frozen with liquid nitrogen and stored at −80 °C. RNA samples were isolated using the RNeasy Plus Mini Kit (Qiagen). Generation of cDNA, sequencing, quality control of raw count, mapping and counting was performed as described 21 , 60 . The samples used for gene expression analysis were confirmed for viral load by quantitative PCR in lung tissue for expression of N5 specific for Beta variant. Samples with poor RNA quality or no viral load were excluded from further analysis. All of the samples that passed RNA quality control were included in the study. A minimum of three replicates per group was used, and genes with less than 0.1 counts per million (CPM) were filtered out from the study. Normalization was then performed using calcNormFactors, and differentially expressed genes were determined using edgeR 71 . The false-discovery rate (FDR) was calculated using the Benjamini–Hochberg method. For NK cell RNA-seq, adjusted P  < 0.1 (two-sided quasi-likelihood F -test with Benjamini–Hochberg correction) was used for visualization in Fig. 3a . The CPM of each gene was normalized across all of the samples to generate z -scores for heat maps of gene expression. Differentially expressed genes significantly changed in uninfected mice were not included in the analysis. For pathway analysis, gene lists were ranked using log 2 -transformed fold change of differentially expressed gene between two groups. Fibrin-induced macrophage scRNA-seq data were obtained from ref. 21 ( GSE229376 ). GSEA was performed using GSEA v.4.2.3 with 1,000 times permutation and collapsing mouse genes to the chip platform Mouse_Gene_Symbol_Remapping_Human_Orthologs_MSigDB.v7.5.1.chip. The MSigDB gene sets: H: Hallmark and C2: CP: Canonical pathways (KEGG, REACTOME, WikiPathways) were used for pathway analysis. The fibrin NK suppression network was generated using Cytoscape (v.3.7.2) 72 . Using differentially altered pathways generated by GSEA (described earlier), the network was visualized using the default setting of EnrichmentMap.

NK cell depletion and characterization

NK cells were purified from splenocytes of C57BL/6 mice using the NK cell isolation kit (Miltenyi Biotec). NK cells were stimulated with IL-15 (50 ng ml −1 , BioLegend) for 4 days with or without fibrin. Flow cytometry staining and analyses were performed as described previously 21 , 60 . For NK cell surface and intracellular staining, NK cell suspensions were first incubated with TruStain FcX PLUS (S17011E, BioLegend) for 15 min at 4 °C, then stained with surface markers for 30 min at 4 °C. Cells were then fixed and permeabilized using the BD Fixation/Permeabilization Kit (554714, BD). Intracellular markers were incubated for 1 h at 4 °C and analysed using the LSR Fortessa flow cytometer (BD Biosciences) the same day. For IFNγ staining, NK cells were incubated with phorbol 12-myristate 13-acetate (P8139, Sigma-Aldrich) and ionomycin (I0634, Sigma-Aldrich) for 4 h in the presence of brefeldin A (B7651, Sigma-Aldrich) followed by surface staining and fixation/permeabilization protocol described above. Anti-IFNγ antibodies were incubated in perm/wash buffer overnight, and then analysed with LSR Fortessa flow cytometer (BD Biosciences) the same day. Antibodies were as follows: NK1.1-FITC (S17016D, BioLegend, 1:200), IFNγ-PE (XMG1.2, BioLegend, 1:200), granzyme B-PerCP/Cy5.5 (QA16A02, BioLegend, 1:200), Ki-67-PE (16A8, BioLegend,1:200), CD45-Brilliant Violet BUV737(30-F11, BD, 1:200), CD11b-Brilliant Ultraviolet 395 (M1/70, BD, 1:200), CD335-Brilliant Violet 421 (clone 29A1.4, BioLegend,1:100), CD54-PE (YN1/1.7.4, BioLegend, 1:200), CD314-APC (CX5, BioLegend, 1:200), LIVE/DEAD Fixable Aqua Dead Cell Stain Kit (L34957, Thermo Fisher Scientific, 1:500). All data were processed using FlowJo v.10.7.1 (BD Biosciences). Doublets and dead cells were excluded before analysis of NK cell phenotypes. NK cells were gated as CD45 + CD3 − NK1.1 + . For NK cell depletion, anti-mouse NK1.1 (PK136, BioXCell), which depletes NK cells 73 , 74 , 75 , or isotype control IgG2a (C1.18.4, BioXcell) were administered intraperitoneally at 8 mg per kg at 3 and 1 days before infection of 5- to 7-month-old mice.

For bulk RNA-seq analysis of mouse NK cells, purified NK cells from splenocytes of C57BL/6 mice were stimulated with IL-15 (50 ng ml −1 , BioLegend) for 4 days with or without fibrin. NK cells were stained with anti-CD3 (145-2C11, BD, 1:200), anti-NK1.1 (S17016D, BioLegend, 1:200), anti-CD45 (30-F11, BioLegend, 1:200) and aqua live/dead fixable dye on ice for 20 min. The CD45 + CD3 − NK1.1 + live NK cells were sorted into 1.5 ml tubes with 1 ml of Buffer RLT Plus with 1% β-mercaptoethanol. RNA samples were prepared using the RNeasy Plus Micro Kit according to the manufacturer’s instructions. The cDNA library generation, quality control, sequencing and downstream analysis are performed as above.

Sample preparation for MS analysis

Human NK cells were isolated from freshly collected PBMCs (AllCells) using the NK cell Isolation Kit, Human (Miltenyi Biotec). In total, 5 × 10 6 NK cells were plated on each well of a six-well plate treated with or without fibrin for 1 h at 37 °C. Phosphoproteomic analysis was performed as described previously 21 , 32 . The samples were washed twice with cold PBS, lysed in 6 M guanidine hydrochloride (Sigma-Aldrich), then boiled at 95 °C for 5 min, and stored on ice until sonication. Lysed samples were sonicated using a probe sonicator for 15 s at 10% amplitude, and protein was quantified by Bradford assay. Approximately 500 µg of protein sample was used for further processing, starting with reduction and alkylation using a 1:10 sample volume of tris-(2-carboxyethyl) (TCEP) (10 mM final) and 2-chloroacetamide (40 mM final) for 5 min at 45 °C with shaking at 1,500 rpm. Before protein digestion, the 6 M guanidine hydrochloride was diluted sixfold with 100 mM Tris-HCL (pH 8) to permit trypsin activity. Trypsin was then added at a 1:100 (w/w) enzyme:substrate ratio and placed in a thermomixer at 37 °C overnight (16 h) with shaking at 800 rpm. After digestion, 10% trifluoroacetic acid (TFA) was added to each sample to reach a final pH of 2. The samples were desalted using a vacuum manifold with 50 mg Sep Pak C18 cartridges (Waters). Each cartridge was activated with 1 ml 80% acetonitrile/0.1% TFA, then equilibrated with 3 × 1 ml of 0.1% TFA. After sample loading, the cartridges were washed with 3 × 1 ml of 0.1% TFA, and the samples were eluted with 1 × 0.8 ml 50% acetonitrile/0.25% formic acid. The samples were dried by vacuum centrifugation. The High-Select Fe-NTA phosphopeptide enrichment kit (Thermo Fisher Scientific) was used according to the manufacturer’s instructions with minor modifications for phosphopeptide enrichment. In brief, the samples were suspended in approximately one-third of the recommended binding/wash buffer volume (70 µl). After equilibrating the spin column, the resin slurry was resuspended in 210 µl of binding/wash buffer and divided into thirds. Each third of the resin was used for one sample. Tryptic peptides were mixed with the resin in a separate protein LoBind tube (Eppendorf) and incubated for 30 min (at room temperature) on a thermomixer at 800 rpm. The samples were transferred on top of a 20 µl filtered tip, washed three times with binding/wash buffer and once with HPLC-grade water. The bound phosphopeptides were eluted with 70 µl elution buffer, and the pH was brought down immediately to nearly three with formic acid (10% (v/v) in HPLC-grade water). All of the samples were dried by vacuum centrifugation and stored at −80 °C until further analysis.

MS proteomics data acquisition

Dried phosphopeptides were resuspended in 0.1% (v/v) formic acid (Sigma Aldrich) in water (HPLC grade, Thermo Fisher Scientific) and analysed on the timsTOF HT mass spectrometer (Bruker Daltonics), paired with a Vanquish Neo ultra-high-pressure liquid chromatography system (Thermo Fisher Scientific). The samples were directly injected onto a PepSep C18 reverse-phase column (15 cm, 150 µm inner diameter, 100 Å pore size, 1.5 µm particle size with UHP inlet, Bruker Daltonics) connected to a captive spray emitter (ZDV, 20 µm, Bruker Daltonics). Mobile phase A consisted of 0.1% (v/v) formic acid in water (HPLC grade, Thermo Fisher Scientific) and mobile phase B consisted of 0.1% (v/v) formic acid in 100% acetonitrile (HPLC grade, Thermo Fisher Scientific). Peptides were separated on a gradient from 3% to 25% mobile phase B over 47 min, followed by an increase to 45% B over 8 min, then to 95% over 1 min, and held at 95% B for 4 min for column washing at a flow rate of 200 nl min −1 . Eluted peptides were ionized in a CaptiveSpray source (Bruker Daltonics) at 1,700 V. Raw data were acquired in data-independent acquisition coupled with parallel accumulation–serial fragmentation (dia-PASEF) mode with an optimized isolation window scheme in the m / z versus ion-mobility plane for phosphopeptides. The ion accumulation time and ramp times in the dual TIMS analyser were set to 100 ms each. For dia-PASEF, in the ion mobility (1/K0) range 0.6 to 1.50 Vs cm −2 , the collision energy was linearly decreased from 59 eV at 1/K0 = 1.6 Vs cm −2 to 20 eV at 1/K0 = 0.6 Vs cm −2 to collect the MS/MS spectra in the mass range 400.2 to 1,399.3 Da. The estimated mean cycle time for the dia-PASEF windows was 1.38 s. The raw files were processed with Spectronaut (v.18.5, Biognosys) using its library-free DIA analysis with directDIA+ (Deep) search algorithm. Carbamidomethylation (cysteine) was set as a fixed modification for database search. Acetylation (protein N-term), oxidation (methionine), and phosphorylation (serine, threonine, tyrosine) were set as variable modifications. Reviewed human protein sequences (downloaded from UniProt, 6 October 2023) were used for spectral matching. The FDRs for the PSM, peptide and protein groups were set to 0.01, and the minimum localization threshold for PTM was set to zero. For MS2 level area-based quantification, the cross-run normalization option was unchecked (normalization was performed later using MSstats, see below), and the probability cut-off was set to zero for the PTM localization. We detected between 4,000 and 7,000 phosphorylated peptides per sample with an average percentage of phosphorylated to non-phosphorylated peptides of 73%.

Computational analysis of phosphoproteomics

Quantification of phosphorylation differences was performed using artMS as a wrapper around MSstats 76 , through functions artMS::doSiteConversion and artMS::artmsQuantification with the default settings. All peptides containing the same set of phosphorylated sites were grouped and quantified together into phosphorylation site groups. One sample outlier in intensity and peptide detection was discarded before quantitative analysis; unstimulated (mock) 1 h (PRIDE sample ID TOF01641_2_1_1683). For both phosphopeptide and protein abundance MSstats pipelines, MSstats performs normalization by median equalization, no imputation of missing values and median smoothing to combine intensities for multiple peptide ions or fragments into a single intensity for their protein or phosphorylation site group. Lastly, statistical tests of differences in intensity between infected and control timepoints were performed. When not explicitly indicated, we used the default settings for MSstats for adjusted P values. By default, MSstats uses the Student’s t -tests for P value calculation and the Benjamini–Hochberg method of FDR estimation to adjust P values. Kinase activities were estimated using known kinase–substrate relationships from the OmniPath database 77 . Kinase activities were inferred as a z -score calculated using the mean log 2 -transformed fold change of phosphorylated substrates for each kinase in terms of standard error ( Z  = [ M  −  μ ]/s.e.), comparing fold changes in phosphosite measurements of the known substrates against the overall distribution of fold changes across the sample. To compare all phosphorylation sites across experimental groups as previously described 32 , a P value was also calculated from log 2 -transformed fold changes of all detected phosphorylation sites using a two-tailed Z -test method as shown in Fig. 3c , Extended Data Fig. 7b and Supplementary Tables 8 – 10 . Network reconstruction and enrichment analysis of phosphoproteomics data were performed as described previously 22 .

Nanostring analysis

Formalin-fixed paraffin-embedded (FFPE) tissue was scrapped off into a 1.5 ml Eppendorf tube and deparaffinized with 1 ml of xylene for 2 min and then pelleted and washed with 1 ml of 100% ethanol. The samples were pelleted and incubated at room temperature until all of the residual ethanol had evaporated. Tissues were digested and RNA samples were isolated using the RNeasy FFPE Kit (Qiagen). The quantity was determined using the Nanodrop (Thermo Fisher Scientific) and the quality of RNA was determined on the Agilent Bioanalyzer. All of the samples passed quality control (>50% of RNA larger than 250 nucleotides). Gene expression assays were performed on the Nanostring nCounter machine with NS_Mm_HostResponse_v1.0 codeset. The raw data were processed and normalized counts, unadjusted P values and log 2 -transformed fold change values were generated with nSolver using two-tailed unpaired t -tests. For pathway analysis, the normalized counts of each gene were normalized across all of the samples to generate a z -score for heat maps of gene expression. The average z -score for each genotype was used for the heat map. Significantly downregulated genes between the 5B8 and IgG2b treated group ( P  < 0.05) were on clusterProfiler to determine significantly downregulated pathways using the enrichGO function. The top 20 significantly downregulated pathways were used to generate the network.

Stereotactic injection of fibrinogen and spike

Fibrinogen was stereotactically injected into the brain as described previously 35 . Mice were anaesthetized with isoflurane and placed into a stereotaxic apparatus (Kopf Instruments). Alexa Fluor 488 human fibrinogen (Thermo Fisher Scientific) was dissolved in 0.1 M sodium bicarbonate (pH 8.3) at 25 °C to 1.5 mg ml −1 (ref. 78 ), mixed with spike (4.6 mg ml −1 ), spike PVs (0.1 mg ml −1 ), bald PVs (0.1 mg ml −1 ) or PBS control (1:1 ratio), and incubated at 37 °C for 15 min; 1.5 μl of the mixture was stereotactically injected at 0.3 μl min −1 with a 10 μl Hamilton syringe and a 33 gauge needle into the corpus callosum of 4- to 5-month-old C57BL/6 mice 35 . Mice were anaesthetized with avertin and transcardially perfused with 4% PFA in PBS. The brains were removed, post-fixed in 4% PFA overnight at 4 °C, processed with 30% sucrose, cut into 30 μm coronal sections and processed for immunohistochemistry. Images were acquired on the Axioplan II epifluorescence microscope (Zeiss) with Plan-Neofluar objectives (×10/0.3 NA). Images of similar anatomical locations were quantified using NIH ImageJ (v.1.50).

RNA in situ hybridization with immunohistochemistry

RNA in situ hybridization with immunohistochemistry was performed on brain sections from mice infected with Delta using RNAscope Multiplex Fluorescent Assay (ACD Bio) according to the manufacturer’s protocol for FFPE tissue. In brief, tissue was deparaffinized and incubated in 3% hydrogen peroxide for 10 min, then subjected to antigen retrieval by boiling in RNAScope Target Retrieval Solution (ACD Bio) for 1 h. The samples were permeabilized with RNAScope Protease Plus reagent (ACD Bio) for 30 min at 40 °C. RNA probes were hybridized to tissue for 2 h at 40 °C. Oligonucleotide probes for mouse Trem2 , Cst7 and Spp1 were designed by ACD Bio (498711-C3, and 435191-C3, respectively). Probe signals were amplified using the RNAScope Multiplex Fluorescent Reagent Kit v2 (ACD Bio) and detected with TSA Vivid Fluorophore 570 (Tocris, 7526). Tissue sections were stained for one RNA probe and counterstained for IBA1 (234 308, Synaptic Systems, 1:500) using the RNA-Protein Co-Detection Ancillary Kit (ACD Bio). The slides were imaged using the Zeiss Axioplan 2 epifluorescent microscope at ×20 and images were analysed using ImageJ (NIH). IBA1-postive microglia in each image were manually counted. Dense clusters of Trem2 , Cst7 or Spp1 mRNA overlapping with IBA1 signal indicate microglia expressing disease-associated genes.

Statistical analysis

All values are reported as mean ± s.e.m. The Shapiro–Wilk normality test 79 was used to evaluate the normal distribution of the data. The equality of variance assumption was verified for both the responses in the natural and logarithmic scales using the Brown–Forsythe test 80 . Comparisons between two matched-paired groups, where the assumption of normal distribution for the differences of paired responses was met, were performed using paired t -tests. P values for comparisons between two independent groups were calculated using Mann–Whitney U -tests in the case of non-normally distributed data for which the equal variance assumption was not violated. For comparisons involving more than two groups, one- or two-way ANOVA followed by Tukey’s post hoc test for multiple comparisons was used for data meeting normal distribution and equal variance assumptions. When the assumption of equal variance was violated, Welch’s t -tests were applied to log 10 -transformed response values, and the resulting raw P values were corrected for multiple testing using the Holm method 57 . For the survival analysis and weight change data, P values were calculated using the log-rank (Mantel–Cox) test and mixed-effects model, respectively. Sample sizes were determined by previous studies rather than statistical approaches. For all in vivo experiments, mice were randomized and experiments were conducted in a blinded manner to the mouse genotype, antibody or PV administration. Genotype and treatment assignment were revealed after image quantification. For bulk RNA-seq and Nanostring experiments, both mouse genotype and antibody treatment were blinded. SEM imaging and image acquisition were performed blinded to test conditions. Biochemical studies of the binding of fibrinogen to spike were performed in the Akassoglou laboratory and independently validated in the Greene laboratory and Assay Development and Drug Discovery Core with similar results.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The bulk RNA-seq datasets have been deposited at the Gene Expression Omnibus under the SuperSeries accession number GSE268813 . The raw data from EM have been deposited in the Cell Image Library ( http://cellimagelibrary.org/groups/57187 ). The MS proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD049692 . Human type I interferon network is at WikiPathways ( https://www.wikipathways.org/instance/WP4868 ). Macrophage scRNA-seq data used from ref. 21 were obtained from GSE229376 . The structures are available for fibrinogen ( 3GHG ) and for spike ( 6VSB ) were obtained from the PDB. All other data are available in the paper.  Source data are provided with this paper.

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Acknowledgements

We thank D. Srivastava for reading of the manuscript; F. Krammer for spike plasmid; D. Goel, S. Gosgoy, E. L. Ryu, W. Wong, S. Rampersaud, B. Cabriga and the staff at the Gladstone Flow Cytometry, Histology and Light Microscopy Cores for technical assistance; S. Ordway and K. Claiborn for editorial assistance; and M. A. Pierce and R. Givens for administrative support. The Gladstone FACS Core acknowledges US National Institutes of Health (NIH) grant S10 RR028962 and the James B. Pendleton Charitable Trust. This research was supported by Brightfocus Postdoc Fellowship Award A2021019F (Z.Y.); Kaganov Scholarship for Excellence in Neuroscience (Z.Y.); UCSF Immunology NIH/NIAID T32 AI007334 (E.G.S.); National Multiple Sclerosis Society FAN-2008-37045 (E.G.S.); Whitcome Pre-Doctoral Fellowship (S.K.M.); Howard Hughes Medical Institute Gilliam Fellowship (S.K.M.); NIH/TIMBS T32HL094274 (P.E.R.C.); NIH/NINDS K99 NS126707 (A.S.M.); NIH R01 HL128503-09; the Howard Hughes Medical Institute (K.R.-H.); NIH grants U24 NS120055, R24 GM137200, R01 GM138780 and S10 OD021784 (M.H.E.); National Science Foundation NSF2014862-UTA20-000890 (M.H.E.); NIH/NIAID K99AI163868 (M.B.); National Institute of Allergy and Infectious Diseases—Host Pathogen Map Initiative grant U19AI135990 (M.O. and N.J.K.); gifts from QCRG philanthropic donors (N.J.K); the James B. Pendleton Charitable Trust (W.C.G. and M.O.); The Roddenberry Foundation (W.C.G. and K.A.); philanthropic gifts from Edward and Pearl Fein, Robert Hamwee, the Ray and Dagmar Dolby Family Fund, and the Simon Family Trust (K.A.); The Foundation for a Better World (K.A.); and National Institutes of Health grants RF1 AG064926 and R35 NS097976 (K.A.).

Author information

These authors contributed equally: Jae Kyu Ryu, Zhaoqi Yan, Mauricio Montano, Elif G. Sozmen, Karuna Dixit, Rahul K. Suryawanshi

Authors and Affiliations

Center for Neurovascular Brain Immunology at Gladstone and UCSF, San Francisco, CA, USA

Jae Kyu Ryu, Zhaoqi Yan, Elif G. Sozmen, Karuna Dixit, Troy N. Trevino, Reshmi Tognatta, Yixin Liu, Renaud Schuck, Lucas Le, Hisao Miyajima, Andrew S. Mendiola, Nikhita Arun, Brandon Guo, Eilidh MacDonald, Oliver Aries, Aaron Yan, Olivia Weaver, Mark A. Petersen, Rosa Meza Acevedo, Maria del Pilar S. Alzamora, Igor F. Tsigelny & Katerina Akassoglou

Gladstone Institute of Neurological Disease, San Francisco, CA, USA

Jae Kyu Ryu, Zhaoqi Yan, Elif G. Sozmen, Karuna Dixit, Anke Meyer-Franke, Troy N. Trevino, Reshmi Tognatta, Yixin Liu, Renaud Schuck, Lucas Le, Hisao Miyajima, Andrew S. Mendiola, Nikhita Arun, Brandon Guo, Eilidh MacDonald, Oliver Aries, Aaron Yan, Olivia Weaver, Mark A. Petersen, Rosa Meza Acevedo, Maria del Pilar S. Alzamora & Katerina Akassoglou

Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

Jae Kyu Ryu, Elif G. Sozmen & Katerina Akassoglou

Gladstone Institute of Virology, San Francisco, CA, USA

Mauricio Montano, Rahul K. Suryawanshi, Yusuke Matsui, Ekram Helmy, Taha Y. Taha, Melanie Ott & Warner C. Greene

Michael Hulton Center for HIV Cure Research at Gladstone, San Francisco, CA, USA

Mauricio Montano, Yusuke Matsui, Ekram Helmy, Taha Y. Taha, Melanie Ott & Warner C. Greene

Department of Microbiology, Immunology and Molecular Genetics (MIMG), University of California Los Angeles, Los Angeles, CA, USA

Prashant Kaushal, Sara K. Makanani & Mehdi Bouhaddou

Institute for Quantitative and Computational Biosciences (QCBio), University of California Los Angeles, Los Angeles, CA, USA

National Center for Microscopy and Imaging Research, Center for Research on Biological Systems, University of California San Diego, La Jolla, CA, USA

Thomas J. Deerinck & Mark H. Ellisman

Department of Biology, Stanford University, Stanford, CA, USA

Pamela E. Rios Coronado & Kristy Red-Horse

Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA

Min-Gyoung Shin, Ayushi Agrawal, Reuben Thomas, Michela Traglia, Alexander R. Pico & Nevan J. Krogan

Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA

Olivia Weaver & Mark A. Petersen

San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA

Valentina L. Kouznetsova & Igor F. Tsigelny

CureScience Institute, San Diego, CA, USA

Department of Neurosciences, University of California San Diego, La Jolla, CA, USA

Igor F. Tsigelny & Mark H. Ellisman

Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA

Kristy Red-Horse

Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA

Nevan J. Krogan

Quantitative Biosciences Institute (QBI), University of California San Francisco, San Francisco, CA, USA

COVID-19 Research Group (QCRG), University of California San Francisco, San Francisco, CA, USA

Nevan J. Krogan & Melanie Ott

Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

Melanie Ott & Warner C. Greene

Chan Zuckerberg Biohub, San Francisco, CA, USA

Melanie Ott

Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA

Warner C. Greene

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Contributions

Conceptualization: K.A., J.K.R. and W.C.G. Performed experiments and analysed data: J.K.R., Z.Y., M.M., E.G.S., K.D., R.K.S., Y.M., E.H., P.K., S.K.M., T.J.D., A.M.-F., P.E.R.C., T.N.T., R. Tognatta, Y.L., H.M., S.K.M., H.M., N.A., O.W., M.A.P., R.M.A., M.d.P.S.A., V.L.K. and I.F.T. Performed experiments: E.M., O.A. and A.Y. Analysed data: M.-G.S., L.L., R.S., A.S.M., B.G., T.Y.T., A.A., R. Thomas, M.T., A.R.P., K.R.-H., M.H.E., N.J.K., M.B., M.O., W.C.G. and K.A.; K.R.-H., M.H.E., N.J.K., M.B., M.O., W.C.G. and K.A. supervised research. K.A., J.K.R., E.G.S., K.D. and Z.Y. wrote the original draft. All of the authors contributed to reviewing and editing the manuscript.

Corresponding authors

Correspondence to Warner C. Greene or Katerina Akassoglou .

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Competing interests.

K.A. is listed as an inventor on US patents 7,807,645, 8,569,242, 8,877,195 and 8,980,836, covering fibrin antibodies, submitted by the University of California. K.A. and J.K.R. are listed as co-inventors on US patent 9,669,112 covering fibrin in vivo models, and US patents 10,451,611 and 11,573,222 covering in vitro fibrin assays submitted by Gladstone Institutes. K.A., J.K.R., M.M. and W.C.G. are listed as co-inventors on US patent 12,016,934 covering the COVID-induced thromboinflammation model and US patent application 18/267,710 for use of fibrin immunotherapy in COVID-19 submitted by Gladstone Institutes. K.A. is a co-founder and scientific advisor of Therini Bio. K.A. has served as a consultant for F. Hoffman-La Roche not related to this study. W.C.G. is a co-founder and shareholder in InvisiShield Technologies, but work in this company has no overlap with the topic or findings presented in this paper. M.O. is a founder of DirectBio and is on the scientific advisory board of InvisiShield, but both are scientifically unrelated to this study. The Krogan Laboratory has received research support from Vir Biotechnology, F. Hoffmann-La Roche and Rezo Therapeutics unrelated to this study. N.J.K. has a financially compensated consulting agreement with Maze Therapeutics, is the president and is on the board of directors of Rezo Therapeutics, and is a shareholder in Tenaya Therapeutics, Maze Therapeutics, Rezo Therapeutics, GEn1E Lifesciences and Interline Therapeutics, but all are unrelated to this study. Their interests are managed in accordance with their respective institutions’ conflict of interest policies. The other authors declare no competing interests.

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Extended data figures and tables

Extended data fig. 1 fibrin interaction and colocalization with spike..

a , Binding ELISA of Spike S1(N501Y) to fibrin. Dissociation constants (K d ). Representative curvefits from two independent biological experiments in duplicates. b , Spike overlap with perivascular fibrin(ogen) deposition in lung of Beta-infected WT mice at 3 d.p.i. The 51% of the calculated proportion of fibrin that colocalizes with Spike protein is significantly higher than the 23% predicted if the correlation were random. Fisher’s exact test (two-tailed); n  = 78 images from 5 mice (Methods). Representative confocal images are shown. Scale bar, 200 μm. c , Scatter plot of positive correlation of fibrinogen and Spike immunoreactivity in n  = 78 images from 5 mice, Pearson correlation two-tailed (Methods). d . 3D reconstruction of light sheet acquisitions of whole lung tissue from an Alexa546-fibrinogen and Alexa647-Spike S1(N501Y)-injected WT mouse following 3DISCO tissue clearing. Two representative focal fibrinogen deposits from n  = 3 mice were selected for 3D visualization. Volumetric rendering reveals close interactions between fibrinogen deposits (green) and trimeric spike (magenta), confirming colocalization. Scale bars, 100 μm (top), 300 μm (bottom). e , Fibrinogen crystal structure (PDB: 3GHG ) with mapped peptides (red). Proximity of peptides γ 163-181 and γ 364-395 (inset). f , Peptide array mapping with immobilized peptides of SARS-CoV-2 Spike blotted with fibrinogen and fibrinogen γ chain. Heatmap of signal intensity showing binding sites (white-orange) within the S1-NT Spike domain. Key indicates fluorescence intensities signal values from low (white) to high (orange). Schematic indicating Spike domains and amino acid sequence.

Extended Data Fig. 2 Computational modelling of fibrinogen-Spike protein interactions.

a , Predicted computational complex model using crystal structure of fibrinogen (PDB ID: 3GHG ) and cryoEM structure of Spike (PDB ID: 6VSB ). b , Surface of this complex. c , Schematic representation of intermolecular interface of this predicted model. d , 3D representation of interface shown by side chain of the few residues involved in interaction. e , Intermolecular interface showing atom-wise interaction with all the residues involved in the interaction including a hydrogen bond highlighted in green dashed line using LigPlot +, v.2.2. https://www.ebi.ac.uk/thornton-srv/software/LigPlus/ .

Extended Data Fig. 3 Fibrin clot ultrastructure and Spike protein interactions.

a , Topographic visualization of fibrin fibre surface in SEM images of fibrin clots in healthy human donor plasma in the presence of Spike. b , SEM of fibrin clots in human plasma in the presence of Spike. x4000 magnification. Images representative of n  = 3 independent biological replicates quantified in ( c ) and Fig. 1f . c , Fibre radius proportion less than 0.05 µm (boxplot) and intersection density (bar plot) in plasma or plasma with Spike. Generalized linear mixed effects model (boxplot) and two-sample two-sided Welch t-test (bar plot). n  = 25 (plasma), n  = 28 (plasma with Spike) images from n  = 3 biologically independent experiments quantified in Fig. 1f . Images from biologically independent experiments are indicated by different colour dots in boxplot. Box indicates the interquartile range (IQR) and whiskers denote the 1.5 × IQR. d , Alanine scan mutagenesis peptide array. Fibrin peptide γ 377-395 was subjected to double-alanine scanning mutagenesis and incubated with His-tagged recombinant Spike. Signal intensity bar graph of the binding of Spike to sequential Ala-Ala substituted peptides (red). Control signal is shown in blue. Residues with low signal intensity upon Ala-Ala substitution are required for binding and highlighted in yellow. e . ELISA of 5B8-huFc or huIgG1 isotype control pre-incubated with fibrin versus the Spike for binding to fibrin. Data are mean ± s.e.m from three biologically independent experiments. f . Iba-1 immunoreactivity in brain following stereotaxic co-injection of fibrinogen with PBS or Spike in WT mice. Scale bar, 50 µm. Data are from n  = 6 mice per group. One-way ANOVA with Tukey’s multiple comparisons test. All data are mean ± s.e.m.

Extended Data Fig. 4 Lung microscopy from SARS-CoV-2 infection.

a , Microscopy of hematoxylin and eosin staining from lung of WT, Fga –/– and Fgg γ390–396A mice after Beta infection or uninfected (UI) WT mice. Representative images from UI n  = 4; Infected, WT n  = 5, Fga –/– n  = 5 and Fgg γ390–396A n  = 4 mice. Scale bar, 200 μm b , Microscopy of fibrin(ogen) (red), Spike (green), Mac-2 (green), and gp91-phox (red) immunoreactivity in the lung from UI mice. Nuclei are stained with DAPI (blue). Scale bar, 70 μm. Representative images from n  = 3 mice per group. c , Microscopy for 4-HNE in lung from UI n  = 5 and Beta-infected n  = 5 WT, n  = 5 Fga –/– , n  = 4 Fgg γ390–396A mice. One-way ANOVA with Tukey’s multiple comparisons test. All data are mean ± s.e.m. Scale bars, 100 μm.

Extended Data Fig. 5 Suppression of the human type I interferon network in Fga – / – mice after SARS-CoV-2 infection.

Overlay of the differentially expressed genes in the lung from Beta-infected Fga –/– mice on the “Type I interferon induction and signalling during SARS-CoV-2 infection - Homo sapiens” pathway. Gene nodes are coloured with red-blue gradient to indicate degree of log2 fold change in gene expression between WT and Fga – / – mice. Red borders indicate statistical significance of unadjusted P  < 0.05 calculated by two-sided quasi-likelihood F-test implemented in edgeR. https://new.wikipathways.org/pathways/WP4868.html .

Extended Data Fig. 6 Lung pathology and viral titers after SARS-CoV-2 infection.

a , Microscopy of NK1.1 in lung after Beta-infection at 3 d.p.i. n  = 5 mice per group. b , Microscopy of N protein in lung after infection at 3 d.p.i; Infected, n  =5 WT, n  = 5 Fga –/– , n  = 4 Fgg γ390-396A mice. c , Box-and-whisker plots showing the number of PFUs propagated from lung homogenates of infected animals on Vero cells. PFU/ml from lung homogenates of Beta-infected n  = 10 WT, n  = 10 Fga – / – and n  = 9 Fgg γ390–396A mice at 3 d.p.i. P values shown by Welch two-sample two-sided t-test followed by multiple correction testing using the Holm procedure. Box indicates the interquartile range (IQR) and whiskers denote the 1.5 × IQR. a, b , One-way ANOVA with Tukey’s multiple comparisons test. All data are mean ± s.e.m. Scale bars, 100 μm.

Extended Data Fig. 7 NK cell responses to fibrin stimulation.

a , Volcano plots of DEGs from bulk RNA-seq of fibrin-stimulated mouse primary NK cells for four days. n  = 3 mice per group. The cutoffs were Log2 fold change > 0.25, adjusted P (FDR) < 0.1 by two-sided quasi-likelihood F-test in edgeR with the Benjamini-Hochberg method. b , Differential phosphorylation site intensities between Fibrin- and IL-15-treated NK cells isolated from PBMCs. Phosphorylation sites grouped based on upstream kinases annotated in the OmniPath network database of kinase-substrate relationships. Red boxes indicate a significant shift in kinase-specific substrate regulation. Unadjusted P  < 0.05 calculated from log2 fold changes of n  = 8,054 phosphorylation sites between conditions derived from 2 (mock), 3 (fibrin) and 2 (IL-15) biologically independent experiments (two-tailed z-test, Methods). Box indicates the interquartile range (IQR) and whiskers denote the 1.5 × IQR. c , Downregulated network of phosphoproteomic interaction in fibrin-treated human NK cells for 1 h compared to IL-15. Colour of kinase represents z-score of kinase activity and colour of substrates represents log2FC (Methods). d , Flow cytometry of mouse NK cells fibrin-stimulated for four days. Ki-67 (%), granzyme B (MFI) and IFN-γ (MFI), n  = 8 mice per group. Two-tailed paired Student’s t-test. e , Flow cytometry of fibrin-stimulated mouse NK cells for four days at concentrations indicated. NKp46 (MFI), NKG2D (MFI). n  = 4 mice per group. One-way ANOVA with post-hoc analysis by Sidak’s multiple comparisons. Data are mean ± s.e.m. f , Flow cytometry of NK cells fibrin-stimulated for 1, 2, and 4 days. NKG2D (MFI), CD54 (MFI), NKp46 (MFI), and granzyme B (%). n  = 4 mice per group. Two-tailed paired Student’s t-test. g , Kinase activities inferred from global mass spectrometry phosphoproteomics of NK cells and macrophages (data from Mendiola et al. 21 ) unstimulated (Mock) or Fibrin-treated for 1 h. Gating strategy is shown in Supplementary Fig. 2 .

Extended Data Fig. 8 NK cell depletion in vivo.

a-d , Microscopy of granzyme, 4-HNE, N protein and gp91-phox in lung from Beta-infected WT, Fga –/– , and Fgg γ390-396A mice given anti-NK1.1 or IgG2a n  = 5 mice per group. Uninfected (UI), n  = 4 mice. Scale bars, 50 μm (a, d), 200 μm (b, c). a-c , Welch two-sample t-test (two-sided) followed by multiple correction testing using the Holm procedure. Unadjusted P values ( b ). d , Two-way ANOVA with Tukey multiple comparison correction. All data are mean ± s.e.m.

Extended Data Fig. 9 Production of PVs and in vivo characterization.

a , Spike PV production (Methods). b , Immunoblot of Spike expression in PVs blotted with anti-Spike, anti-p24 Gag (detecting p55) and anti-Vpr. Spike PVs expressed S1, S2, cleaved S1 and Spike multimeric forms. PVs express comparable levels of the proviral backbone indicated by HIV Env VPs (Vpr). c , Fibrinogen immunoprecipitation with PVs blotted with anti-Spike or anti-fibrinogen. d , ROS production in fibrin-stimulated BMDMs (24 h) with PVs. n  = 3 biologically independent experiments. e , Fibrin(ogen) from lungs of n  = 6 WT mice per group 24 h after PV injection. Scale bars, 200 µm and 50 µm (inset). Welch two-sample t-test (two-tailed) followed by Holm multiple correction testing. f . Confocal microscopy of Spike (green) and fibrin(ogen) (red) in lung 24 h after Spike PVs injection; orthogonal views of the y/z and x/z planes show the localization of fibrinogen and Spike. Scale bar, 50 μm. Scatter plot shows correlation of fibrinogen and Spike in n  = 24 images from three mice, Pearson correlation (Methods). g , gp91-phox (red) and Mac-2 (green) in lungs 24 h after injection of n  = 6 mice (Bald, Spike or HIV-ENV PVs) and n  = 3 uninjected controls (UI). Scale bars, 100 μm. h , Mac-2 (green) and gp91-phox (red) in lungs from WT and Fga –/– mice after Bald PVs injection. Scale bar, 70 μm. Representative images from n  = 6 mice per group. Quantification in Fig. 4c . i , Iba-1 in corpus callosum after stereotactic co-injection of fibrinogen with PBS, Bald PVs or Spike PVs. Scale bar, 50 µm. n  = 6 mice per group. Representative immunoblots from two ( b ) or three ( c ) biologically independent experiments. d, g, i , One-way ANOVA with Tukey’s multiple comparisons test. All data are mean ± s.e.m. For gel source data, see Supplementary Fig. 1 .

Extended Data Fig. 10 5B8 target engagement in the brain and effects on lung pathology in Beta infection.

a , Microscopy of fibrinogen and NK1.1 in lungs from prophylactic 5B8- and IgG2b-treated Beta-infected WT mice 3 d.p.i. Scale bars, 100 μm. Infected, n  = 10 mice per group; uninfected (UI), n  = 4 mice. Two-tailed Mann-Whitney test (fibrin(ogen)), one-way ANOVA with Tukey multiple comparisons (NK1.1). b , Box-and-whisker plots showing the number of PFUs propagated from lung homogenates of infected animals on Vero cells at 3 d.p.i. PFU/ml from lung homogenates of Beta-infected WT mice given prophylactically 5B8 or IgG2b. n  = 10 per group. Two-tailed Mann-Whitney. Box indicates the interquartile range (IQR) and whiskers denote the 1.5 × IQR. c , Microscopy of 4-HNE in lungs from therapeutic 5B8- and IgG2b-treated WT mice after Beta infection at 7 d.p.i. Scale bars, 100 μm. n  = 12 (IgG2b) or n  = 11 (5B8). Two-tailed Mann-Whitney test. d , Microscopy of brain sections from Beta-infected WT (left) and UI control (right) given i.p. injection of 30 mg/kg 5B8-huFc showing the spatial co-localization (yellow) between 5B8-huFc detected with FITC-human IgG (green), and fibrin deposition detected with antibody to fibrin(ogen) (red) at 7 d.p.i. The 5B8-huFc antibody was used in target engagement studies to enable in vivo detection in the mouse by human FITC-IgG. Scale bars, 80 μm. Data are representative of n  = 3 mice. All data are mean ± s.e.m.

Extended Data Fig. 11 Disease-associated genes in microglia in Delta-infected mice.

a , Representative RNAscope images of Trem2 , Cst7 , and Spp1 (red) mRNA expression in Iba-1 immunoreactive microglia (green) within the hippocampus of Delta-infected K18-hACE2 mice. b , Quantification of Trem2, Cst7, and Spp1 mRNA expression as percentage of total Iba-1+ cells in the hippocampus. Data are from n  = 5 mice per group. Two-tailed Mann-Whitney test. Scale bars, 20 μm. All data are mean ± s.e.m. UI, uninfected.

Extended Data Fig. 12 Fibrin-targeting immunotherapy protects from SARS-CoV-2 Spike neuroinflammation.

a , Confocal microscopy of fibrin(ogen) and MBP in brains from Delta-infected K18-hACE2 mice at 3 d.p.i. Representative images from five mice per group. UI, uninfected. Delta-infected K18-hACE2 mice were given IgG2b isotype control. Scale bar, 60 μm. b , Box-and-whisker plots showing the number of PFUs propagated from lung homogenates of infected animals on Vero cells at 3 d.p.i. PFU/ml from lung homogenates of Delta-infected K18-hACE2 mice given prophylactically 5B8 ( n  = 4) or IgG2b ( n  = 5). Two-tailed Mann-Whitney. Box indicates the interquartile range (IQR) and whiskers denote the 1.5 × IQR. c , Confocal microscopy of Iba-1 and Iba-1 and MBP in brains from Delta-infected K18-hACE2 mice treated prophylactically with 5B8 or IgG2b. Scale bars, 50 μm (top) and 100 μm (bottom). UI, n  = 5 mice; infected, 5B8 n  = 4 mice and IgG2b n  = 5 (MBP), n  = 4 (Iba-1) mice. One-way ANOVA with Tukey’s multiple comparisons test or two-tailed Mann-Whitney test. d , Confocal microscopy of Iba-1 immunoreactivity in cortex from Delta-infected K18-hACE2 mice treated prophylactically with 5B8 or IgG2b. Representative images from five uninfected and four infected mice per group. Right-most image shows magnification of the white box. Scale bar, 100 μm; 50 μm (inset). e , GSEA analysis of the top 20 down-regulated pathways in brain tissues at 9 d.p.i from Delta-infected K18-ACE2 mice treated therapeutically with 5B8 versus IgG2b, starting at 1 d.p.i then every 2 days for 8 days. f , Microscopy of Mac-2 and gp91-phox in lungs from Spike PV-injected WT mice at 24 h given prophylactically 5B8 or IgG2b. Scale bars, 50 μm. n  = 6 mice per group. Two-tailed Mann-Whitney test. All data are mean ± s.e.m.

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Ryu, J.K., Yan, Z., Montano, M. et al. Fibrin drives thromboinflammation and neuropathology in COVID-19. Nature (2024). https://doi.org/10.1038/s41586-024-07873-4

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Received : 13 February 2023

Accepted : 24 July 2024

Published : 28 August 2024

DOI : https://doi.org/10.1038/s41586-024-07873-4

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