OBJECTIVES:Transcranial focused ultrasound stimulation (tFUS), also known as low-intensity focused ultrasound pulsation, may noninvasively modulate key brain networks subserving consciousness and carries promise as a novel interventional tool to stimulate recovery of consciousness in individuals with disorders of consciousness (DoC) after severe brain injury. However, the novelty of this approach and its attendant effects raise underexplored ethical considerations warranting explicit attention to ensure responsible development and deployment in clinical research and practice. Our objectives here are to develop an ethical framework for responsible translation of this emerging neurotechnology. MATERIALS AND METHODS:To identify and critically evaluate the responsible use of tFUS to promote recovery of consciousness in individuals with DoC, we evaluate ethical considerations through the lens of the principles of biomedical ethics, coupled with thematic, normative, and philosophical analysis. We describe safeguards for innovation and clinical translation in this domain. RESULTS:Specific ethical domains evaluated include respect for autonomy, beneficence, nonmaleficence, justice, enrollment considerations, and fair study access, which synergistically inform an ethical framework for stakeholders involved in pioneering and early use of tFUS. CONCLUSIONS:To provide support for clinicians and investigators navigating complex decision-making at the nexus of neurotechnology and neuroethics, we propose a practical checklist for ethical implementation and evaluation of tFUS studies for patients with DoC.

Approximately 25% of patients with severe brain injuries who appear unresponsive on the bedside behavioral examination are covertly conscious-able to volitionally modulate their brain activity using task-based functional MRI or task-based EEG. Since the first description of covert consciousness in 2006, nearly all reports of covert consciousness have been in patients who were behaviorally awake, with their eyes open. These observations led to a widespread assumption that cortical activation associated with consciousness required a foundation of subcortical connections to produce wakefulness. We report the case of an 80-year-old woman with acute severe traumatic brain injury, whose level of consciousness was evaluated in the intensive care unit with a multimodal diagnostic protocol integrating behavioral, task-based fMRI, and task-based EEG assessments. Despite behavioral evidence of coma on every behavioral examination, the task-based fMRI and task-based EEG assessments both indicated the presence of covert consciousness. These observations suggest an unexpected phenomenon whereby awareness (i.e., volitional brain activity) can be dissociated from wakefulness (i.e., eye opening) in the human brain. The possibility that individuals who outwardly appear comatose may harbor covert consciousness carries both clinical and ethical weight, and unsettles long-held assumptions, embedded in the very term coma, that patients diagnosed as such are uniformly unaware and insensate.

The human brain operates through large-scale networks whose subcortical components are critical for consciousness, emotion, and cognition. While cortical network connectivity has been mapped with increasing precision, subcortical network mapping has lagged far behind due to two fundamental barriers: the overlapping and correlated nature of brain networks, which conventional analytic methods cannot disentangle, and the inherently low signal-to-noise ratio of functional imaging data within the subcortex. As a result, no consensus or normative atlas for subcortical functional brain connectivity exists-a critical gap that has impeded both basic neuroscience and the development of targeted neuromodulatory therapies. In this work, we address these barriers using NASCAR, a tensor decomposition method explicitly designed to separate overlapping and correlated networks, applied to resting-state functional MRI data from 1,000 healthy individuals in the Human Connectome Project. This approach enabled us to fractionate four large-scale brain networks into 15 highly reproducible subnetworks spanning both cortical and subcortical structures, revealing their sites of neuroanatomic overlap and defining a normative whole-brain functional atlas grounded in subcortical connectivity. As proof of principle for the translational potential of this framework, we show that individual patterns of subnetworks predict levels of consciousness in patients with severe traumatic brain injury. By establishing a gold-standard, openly accessible reference for subcortical functional brain organization, this work expands the landscape of human brain network mapping and opens avenues for precision targeting in the treatment of a broad spectrum of neurological and psychiatric disorders.

Clinical interventions and neuroimaging in the subcortex require anatomical definitions that exceed the resolution and anatomical detail of currently available deformable brain atlases. Here, we introduce a high-resolution human brain atlas comprising 95 manually segmented grey and white matter structures as well as 82 white matter tracts compiled from a multitude of resources including ex-vivo MRI, histology, fibre dissections, and neuroanatomy textbooks. The atlas is defined at an isotropic resolution of 100 μm and can be precisely deformed to individual subject brain anatomy. By providing precise definitions of both grey and white matter structures within and around the basal ganglia, thalamus, subthalamus, midbrain and cerebellum, the atlas provides a foundational resource for stereotactic surgery and subcortical brain imaging research, as well as for development of next-generation neuromodulation strategies.

For active-duty military Service Members and Veterans, there are currently no reliable diagnostic tests for brain injury associated with repeated exposure to explosive blasts, blunt head impacts, or acceleration/deceleration g-forces. In this Commentary, we explore the current state of the science in military brain injury and propose that the development of new diagnostic tests is essential to optimize military brain health care. We advocate for a comprehensive, multidisciplinary effort to identify advanced neurotechnologies that detect the "invisible wounds of war." Diagnosis is the foundation upon which prevention and therapy will be built.

Accurate neuroprognostication of cardiac arrest survivors who are initially comatose after restoration of spontaneous circulation is crucial for guiding patient management. Because hypoxic-ischaemic injury is typically diffuse, damage to a network of brain regions is likely involved in the patient's disorder of consciousness. To quantify these complex brain network changes, graph theoretical methods were applied. We hypothesize that structural connectivity metrics may provide insights into which patients will likely recover consciousness. Eighteen comatose patients (50 ± 22 years, 44% male) and four healthy participants (40 ± 20 years, 50% male) underwent multi-shell high angular diffusion MRI as part of a prospective study. Structural connectivity matrices were constructed using probabilistic tractography to measure the likelihood of connections between anatomical regions. Network topology alterations were quantified using clustering coefficient, global efficiency and degree. Hub index analysis was performed to explore the impact of anoxic injury on high-degree hubs. Network parameters were compared between patients with arousal recovery (AR, eye-opening to auditory or noxious stimulation) and without arousal recovery (No AR). Analyses were repeated for AR patients who achieved emergence from the minimally conscious state (EMCS) within one-year post-cardiac arrest and AR patients who did not achieve EMCS (AR'). Significant differences were observed between the Controls, AR and No AR for all four metrics (Kruskal-Wallis Tests, P < 0.05). Worsening disorders of consciousness were associated with decreasing brain complexity (Kendall's tau, P<0.01). Post-hoc testing showed Control values were significantly greater than No AR for all metrics (Wilcoxon rank sum, P < 0.05). Control values were greater than AR for all metrics (P < 0.05), except the clustering coefficient (P = 0.36). AR was significantly greater than No AR for all metrics (P < 0.05), except for the hub index (P = 0.12). Notable differences between AR' and Controls were observed for all metrics (P < 0.05), except clustering coefficient (P = 0.11). No significant differences were found between AR' and No AR groups. In contrast, for all metrics, EMCS values were not significantly different compared with the Controls but were significantly different than the No AR cohort values (P < 0.05). The hub index analysis revealed disproportionate damage to high-degree nodes such as the thalamus, putamen and precuneus, further linking topological disruption to the severity of outcomes. This study highlights the potential of graph theoretical measures of structural connectivity to guide decisions in the care of comatose cardiac arrest patients. By bridging structural connectivity with clinical outcomes, this research provides valuable insights into the neural mechanisms underlying consciousness and recovery after cardiac arrest.

Early detection of consciousness in critically ill patients with severe brain injuries can profoundly impact prognostication and clinical care decisions. Advanced multimodal protocols to detect signs of consciousness include standardized behavioral assessments, task-based functional magnetic resonance imaging (fMRI), and task-based electroencephalography (EEG). However, these approaches have limited diagnostic sensitivity because patients may lack auditory function, attention, language, or other cognitive capacities required to perform a task or process a sensory stimulus, even if they are conscious. Transcranial magnetic stimulation EEG (TMS-EEG) has the potential to overcome these limitations by directly engaging corticothalamic circuits to compute the perturbational complexity index (PCI), an emerging indicator of consciousness. To date, TMS-EEG studies have focused on patients in the subacute or chronic stage of recovery from severe brain injury. Here, we report the proof-of-concept application of TMS-EEG for a critically ill patient in the acute stage of brain injury, in which multimodal assessments suggested a vegetative state/unresponsive wakefulness syndrome. We demonstrate that TMS-EEG may detect signs of consciousness that elude current advanced evaluations, showing the feasibility of TMS-EEG as part of a multimodal protocol for assessing consciousness in the intensive care unit.

Patients with disorders of consciousness (DoC) characteristically lack decision-making capacity, a central challenge for shared decision-making, as surrogate decision-makers must navigate the uncertainties of making proxy care decisions. The element of uncertainty is especially prominent considering growing recognition of cognitive motor dissociation or covert consciousness, attributable to advances in neurotechnologies that enable the detection of signatures of responsiveness and recovery capacity that evade routine bedside detection. Professional society guidelines now recommend use of advanced neurotechnologies for some patients, marking their transition from investigational into guideline-directed clinical tests. Yet, advanced neurotechnologies themselves introduce uncertainties to the calculus of shared decision-making, particularly given a paucity of guidance on clinical translation. Through semistructured interviews, we examined attitudes of clinicians and family members of patients with potential covert consciousness during three stages of conversation regarding translation of advanced neurotechnologies into DoC practice. Although clinicians described weighing clinical, prognostic, and logistical factors when deciding to introduce advanced testing, most family members regarded clinicians as ethically obligated to offer advanced neurotechnologies in DoC assessment. There was near consensus that results of advanced neurotechnologies must be shared, even in research contexts. The majority of clinicians and family members posited that results of advanced neurotechnologies should be communicated in ways that are sensitive to families’ understanding, background, receptiveness to information, and anticipated decision-making role, and they valued transparency regarding the limitations and uncertainties inherent to these modalities. Clinicians placed higher weight on positive rather than negative results. Half of family members reported that results of advanced neurotechnologies impacted care decisions for their loved ones with DoC. Our findings reveal key points of convergence and divergence between clinicians and family members throughout stages of decision-making, grounding an ethically informed discussion guide that clinicians may use as a roadmap to support shared decision-making in this emerging context.

The cerebral vasculature is central to brain function, with alterations linked to numerous cerebrovascular and neurological disorders. Yet, no single imaging modality can capture the entire cerebral vascular network in humans. Instead, an array of techniques are sensitized to different spatial scales, while trading off resolution for coverage. Magnetic Resonance Imaging (MRI) typically resolves only large pial vessels, while high-resolution microscopy allows micrometer-scale vessels to be mapped over limited spatial extents. These techniques must therefore be combined to obtain a complete mapping of the cerebral angioarchitecture, which underscores the need for automatic, cross-modal vessel segmentation. Here, we introduce VesSynth, a flexible vessel segmentation framework that achieves state-of-the-art accuracy across multiple modalities and spatial resolutions (MR, optical and X-ray imaging), despite being trained entirely on synthetic data. By enabling consistent vascular mapping across scales, this framework paves the way to comprehensive investigation of cerebrovascular organization and its role in health and disease.

Objective:Predicting specific cognitive, psychiatric, and health-related sequelae in patients after acute traumatic brain injury (TBI) remains an important but challenging clinical problem. Acute phase computed tomography (CT) scans acquired show hemorrhagic contusions, a common type of traumatic pathology. However, whether CT-measured contusions predict long-term sequelae is uncertain. Methods:We established a Screening Cohort of patients with acute TBI who received care at a single TBI Model Systems (TBIMS) inpatient rehabilitation facility. Regions of hemorrhagic contusion and edema were labeled on acute brain CT scans using the fully-automated Brain Lesion Analysis and Segmentation Tool (BLAST-CT). We screened 198 outcome variables at 1-year post-injury for association with acute hemorrhagic contusion volume using the Harrell's Concordance index (C-index), controlling for multiple comparisons using 5,000 outcome permutations. Finally, we tested whether the significant associations in the TBIMS database replicated in acute (Transforming Research and Clinical Knowledge in TBI [TRACK-TBI]) and chronic (Vietnam Head Injury Study [VHIS]) external validation cohorts. Results:The TBIMS Screening Cohort included 345 participants (mean ± SD age: 55.7 ± 21.5 years) with median [IQR] contusion volume 2.3 cc [0.1, 14.6]. Among 198 candidate outcome variables, only delayed seizures were significantly associated with acute hemorrhagic contusion volume (C-index = 0.81; P FWE = 0.007). Contusion volume was not significantly associated with commonly-used measures of global functioning like the Glasgow Outcome Scale Extended, (C-index = 0.55; P FWE = 1). Within the screening cohort, 30 ccs was the optimal volume threshold for discriminating patients with versus without delayed seizures (OR 12.6, 95% CI: [4.6, 34.3]). Contusions larger than 30 cc remained significantly associated with delayed seizures in two external cohorts: (TRACK-TBI OR 4.1 [1.5, 11.2]; VHIS OR 3.2 [1.7, 6.2]). Interpretation:Across three cohorts of patients with TBI, CT-derived contusion volume is robustly associated with the development of delayed seizures, in contrast to commonly-used outcomes measuring global functioning. A 30-cc volume threshold can be used to improve epilepsy prediction models and enrich populations for clinical trials.

OBJECTIVE:We developed a continuous prognostic monitoring tool to predict recovery from disorders of consciousness (DoC) following acute brain injury (ABI), utilizing resting-state EEG recorded during routine clinical care. METHODS:Predictive models updating every 5 min were developed using serial neurologic assessments and continuous resting-state EEG to predict future consciousness level at 24-, 48-, and 72-hour time horizons. An ensemble of CatBoost classifiers was utilized for multi-class DoC grade prediction, leveraging a comprehensive set of 242 computed EEG features encoding time, frequency, and time-frequency characteristics. Conventional and confound-isolating cross-validation mitigated biases and increased robustness. Performance was compared across multiple ordinal DoC grade cut-points and time horizons. RESULTS:201 patients met inclusion criteria. Models incorporating EEG outperformed behavioral assessments alone, achieving a mean one-vs-rest AUROC of 0.88-0.89 (EEG + GCS) across 24-72-hour horizons and various trichotomized cut-points, with 95% bootstrap confidence intervals. The most robust features included global field power, theta-band (4-8 Hz) bandpower, beta-band (13-30 Hz) phase-locking value, and spectral entropy. CONCLUSIONS:EEG-augmented models enabled continuous prediction of future DoC grade after ABI. SIGNIFICANCE:Combining EEG with other serial measures during routine clinical care creates a novel paradigm for improved shared decision-making through continuous prognostic monitoring and serial assessment.

Structured acquisition and analysis of individual-level health data in the context of biomedical research can yield novel results with potential clinical or personal relevance to participants. While approaches to returning individual-level research results to study participants in civilian contexts have received some attention, unique ethical considerations informing approaches to sharing military research results, and particularly in research studies involving active-duty Special Operations Forces (SOF), are underexplored. As the number of research studies enrolling active-duty military personnel grows, an ethical framework to guide responsible handling and sharing of individual-level research results in these distinctive contexts is crucial to safeguard the rights and welfare of research participants and to elucidate appropriate practices for investigators. After exploring the landscape of ethical, clinical, legal, and logistical considerations, both motivating and complicating routine sharing of individual-level biomedical research results with active-duty SOF personnel, we propose a framework to guide responsible disclosure of results to capture potential benefits while mitigating possible risks.

Precise prognostication in acute brain injury is limited by a lack of reliable biomarkers of consciousness available to clinicians at the bedside. The ABCD framework is a method of classifying resting-state clinical EEG into categories that reflect levels of thalamocortical network function. ABCD classifications in the intensive care unit (ICU) have been shown to provide diagnostic and prognostic utility for patients with severe brain injuries, but the current gold standard for ABCD classification is visual inspection of power spectra, which is labor-intensive and requires expertise in spectral analysis. Using 4,611 manually classified EEG power spectra, we developed an automated, highly accurate, and well-calibrated convolutional neural net-based classifier of EEG into ABCD categories. The classifier has performance comparable to that of the current gold standard and that outperforms an alternative method of automated spectral analysis. As proof-of-principle for clinical implementation, we apply the classifier to a continuous EEG record from a patient with acute severe traumatic brain injury in the ICU, demonstrating its ability to yield continuous ABCD classifications that capture state fluctuations with high temporal and spatial resolution. The automated ABCD classifier allows for efficient analysis of continuous EEG records, facilitating the translation of the ABCD framework to the bedside for patients with acute severe brain injuries. The ABCD classifier also creates new opportunities to efficiently analyze large EEG datasets and generate new insights into the electrophysiological properties of human consciousness.

Abstract Background There are currently no therapies proven to accelerate recovery of consciousness for patients with acute severe traumatic brain injury (TBI) in the intensive care unit (ICU). Dopaminergic stimulation is a candidate strategy for reactivating subcortical networks that underly consciousness. Methods We performed an open-label, Phase 1 safety and dose-finding study of intravenous methylphenidate (IV MPH) in ICU patients with acute disorders of consciousness (DoC) caused by severe TBI. IV MPH, a potent and rapid-acting dopamine reuptake inhibitor, was administered in daily doses of 0.5, 1.0, and 2.0 mg/kg. The primary outcome measure was the number of adverse events (AEs) at each dose. IV MPH pharmacokinetics were measured for 24 hours after each dose. Pharmacodynamic target engagement – the effect of IV MPH on brain networks – was measured using EEG and resting-state functional MRI (rs-fMRI). A pharmacodynamic response was defined by change-point analysis of EEG and rs-fMRI time-series data. Behavioral responses were assessed using the Coma Recovery Scale-Revised (CRS-R). Results Between August 24, 2020, and April 1, 2024, we screened 488 ICU patients with TBI and enrolled 9 males (age 23-79 years) with acute traumatic DoC: coma (n=3), vegetative state/unresponsive wakefulness syndrome (n=3), and minimally conscious state (n=3). There were no serious AEs at any dose. Mild-moderate AEs observed at 1.0 mg/kg or 2.0 mg/kg included insomnia, emesis, paroxysmal sympathetic hyperactivity, and transaminitis. Maximum plasma MPH concentration ranged from mean (SD) 312.7 (100.6) ng/mL to 1319.5 (433.8) ng/mL and occurred within a median of 7-14 minutes across doses. Pharmacodynamic responses were observed via EEG in 7/8 participants who received 0.5 mg/kg (1/9 did not undergo EEG), 6/9 who received 1.0 mg/kg, and 4/6 who received 2.0 mg/kg. One of two patients who completed rs-fMRI showed a pharmacodynamic response. CRS-R level of arousal increased within 15 min of the IV MPH bolus for 6/9 participants at 0.5 mg/kg, 5/9 at 1.0 mg/kg, and 0/6 at 2.0 mg/kg. Conclusions For patients with acute severe TBI, IV MPH may be safe at daily doses of 0.5-2.0 mg/kg. EEG and rs-fMRI evidence of target engagement, accompanied by rapid behavioral increases in arousal, provides proof-of-principle that IV MPH reactivates subcortical networks underlying arousal, a prerequisite of consciousness. These findings support further evaluation of IV MPH in controlled trials.

OBJECTIVES:Prolonged disorders of consciousness are common in critically ill patients receiving mechanical ventilation and are often attributed to prolonged sedative exposure in the setting of decreased drug clearance and/or reduced metabolism. Here, in a large sample of critically ill COVID patients obtained over a short period, we tested the assumption that prolonged unconsciousness following benzodiazepine and/or propofol sedation can be attributed to residual exposure. Further, we examine associations between clinical variables on time to recovery of consciousness (RoC) as a framework for the broader critically ill population. DESIGN:Retrospective cohort study. SETTING:Massachusetts General Hospital, Weill Cornell Medicine, Columbia University Irving Medical Center. PATIENTS:Seven hundred eighty-four patients with COVID-19 critical illness in Spring-Summer 2020. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We estimated the latest expected RoC (LERoC) using models of sedation exposure that account for sedative-specific pharmacokinetics and critical illness. Our primary exposure variable was a time-weighted dose of analgosedative agents at benzodiazepine and/or propofol cessation; our primary outcome was time to RoC. We estimated relative risks for late RoC (i.e., after LERoC) via multinomial logistic regression. Among individuals with late RoC, we fit a multivariate subdistribution hazard model for time to RoC. Seventy-three percent of patients had RoC before hospital discharge, yet 34% of patients achieving RoC did not do so within pharmacologically plausible sedative elimination times. Patients with late RoC were older and exhibited hypoxemia and acute kidney injury. Patients with dexmedetomidine as an adjunct sedative had a disproportionately larger incidence of early recovery. Patients with early vs. late RoC did not have significantly different discharge dispositions. CONCLUSIONS:In our cohort, the time to RoC was commonly prolonged beyond that expected from sedation exposures alone. These data may aid clinicians and families with expectations of RoC and warrant investigation of alternative determinants of delayed RoC in this population.

Portable, ultra-low-field (ULF) magnetic resonance imaging has the potential to expand access to neuroimaging but currently suffers from coarse spatial and angular resolutions and low signal-to-noise ratios. Diffusion tensor imaging (DTI), a sequence tailored to detect and reconstruct white matter tracts within the brain, is particularly prone to such imaging degradation due to inherent sequence design coupled with prolonged scan times. In addition, ULF DTI scans exhibit artifacting that spans both the space and angular domains, requiring a custom modelling algorithm for subsequent correction. We introduce a nine-direction, single-shell ULF DTI sequence, as well as a companion Bayesian bias field correction algorithm that possesses angular dependence and convolutional neural network-based superresolution algorithm that is generalizable across DTI datasets and does not require re-training (''DiffSR''). We show through a synthetic downsampling experiment and white matter assessment in real, matched ULF and high-field DTI scans that these algorithms can recover microstructural and volumetric white matter information at ULF. We also show that DiffSR can be directly applied to white matter-based Alzheimers disease classification in synthetically degraded scans, with notable improvements in agreement between DTI metrics, as compared to un-degraded scans. We freely disseminate the Bayesian bias correction algorithm and DiffSR with the goal of furthering progress on both ULF reconstruction methods and general DTI sequence harmonization. We release all code related to DiffSR for $\href{https://github.com/markolchanyi/DiffSR}{public \space use}$.

Diffuse axonal injury (DAI) is caused by acceleration-deceleration forces during trauma that shear white matter tracts. Susceptibility-weighted MRI (SWI) identifies microbleeds that are considered the radiologic hallmark of DAI and are used in clinical prognostication. However, this assumption is limited by a lack of systematic radiologic-pathologic correlation studies. Here, we performed ex vivo SWI on three brains from patients who died after severe TBI and assessed axonal injury around SWI microbleeds using immunohistochemistry to the amyloid-beta precursor protein. Axonal injury was present in 64% of microbleeds, indicating a heterogeneous injury response in the white matter.

US Special Operations Forces (SOF) personnel endure repeated blasts throughout training and combat. Recent human postmortem, neuroimaging and blood proteomic work suggest that tau pathology is present following repeated blast exposure. This study aimed to determine whether blood tau markers are associated with brain tau paired helical filaments (PHFs) in SOF personnel. Twenty-eight active-duty SOF completed a positron emission tomography-magnetic resonance imaging scan with the PHF-specific radiotracer fluorine-18 MK6240 ([18F]MK6240) and provided blood samples to measure total tau and phosphorylated tau181 (p-tau181). Whole brain voxel-wise analysis showed that higher total tau in the blood was associated with higher [18F]MK6240 uptake in the left temporal cortex, parahippocampal gyrus and hippocampus. We performed post hoc analyses to assess whether brain or blood tau measures were associated with memory performance. Higher levels of blood total tau and p-tau181 were associated with a longer response time and lower throughput (i.e. fewer accurate responses per minute) during the Code Substitution-Delayed test, a visual memory task in the Automated Neuropsychological Assessment Metrics (ANAM). This study provides preliminary evidence in active-duty SOF that blood total tau is associated with regional [18F]MK6240 uptake in the brain and that blood total tau and p-tau181 are associated with memory performance.

Brainstem white matter (WM) bundles are essential conduits for neural signals that modulate homeostasis and consciousness. Their architecture forms the anatomic basis for brainstem connectomics, subcortical circuit models, and deep brain navigation tools. However, their small size and complex morphology, compared to cerebral WM, makes mapping and segmentation challenging in neuroimaging. As a result, fundamental questions about brainstem modulation of human homeostasis and consciousness remain unanswered. We leverage diffusion MRI tractography to create BrainStem Bundle Tool (BSBT), which automatically segments eight WM bundles in the rostral brainstem. BSBT performs segmentation on a custom probabilistic fiber map using a convolutional neural network architecture tailored to detect small anatomic structures. We demonstrate BSBT's robustness across diffusion MRI acquisition protocols with in vivo scans of healthy subjects and ex vivo scans of human brain specimens with corresponding histology. BSBT also detected distinct brainstem bundle alterations in patients with Alzheimer's disease, Parkinson's disease, multiple sclerosis, and traumatic brain injury through tract-based analysis and classification tasks. Finally, we provide proof-of-principle evidence for the prognostic utility of BSBT in a longitudinal analysis of traumatic coma recovery. BSBT creates opportunities for scalable mapping of brainstem WM bundles and investigation of their role in a broad spectrum of neurological disorders.

Emotional dysfunction is a common consequence of severe traumatic brain injury, yet the mechanisms underlying these symptoms remain poorly understood. This study investigated whether brain network and autonomic mechanisms involved in emotional processing are abnormal in traumatic brain injury. We conducted a cross-sectional study of chronic severe traumatic brain injury (n = 26; age range = 21-73 years; 15 females) and healthy control participants (n = 15; age range = 20-50 years, eight females). We analyzed functional MRI data to assess brain processing of emotionally salient music (joyful and fearful stimuli; n = 15 traumatic brain injury, n = 15 controls), and resting-state functional MRI to measure the functional connectivity of relevant intrinsic brain networks (limbic, salience, and default mode networks; n = 16 traumatic brain injury, n = 15 controls). We additionally measured the pupillary light reflex to assess parasympathetic and sympathetic function (n = 14 traumatic brain injury, n = 11 controls). Individuals with severe traumatic brain injury did not demonstrate the left insula activation elicited by joyful versus fearful musical stimuli seen in healthy controls. Resting-state functional MRI revealed decreased connectivity between the salience network, caudate, and hippocampus in severe traumatic brain injury compared to controls. Exploratory analyses identified reduced connectivity between default mode (bilateral medial prefrontal cortex) and limbic (bilateral amygdala) nodes in traumatic brain injury compared to controls. Pupillary light reflex measurements revealed blunted dark-adaptation responses in individuals with severe traumatic brain injury compared to controls (F(1,24) = 27.4, p < 0.001). Individuals with chronic severe traumatic brain injury show reduced insula activation during emotional stimuli processing, resting connectivity abnormalities in salience, limbic, and default mode networks, and evidence of sympathetic dysfunction. Brain network and autonomic alterations may be potential neural mechanisms of post-traumatic brain injury emotional dysregulation.