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OBJECTIVE: To determine whether consistent command-following (CCF) should be added to the diagnostic criteria for emergence from the minimally conscious state (eMCS). DESIGN: Retrospective cohort study. SETTING: Inpatient rehabilitation hospital. PARTICIPANTS: Patients (N=214) with acquired brain injury resulting in disorders of consciousness (DoC) admitted to a specialized rehabilitation program. MAIN OUTCOME MEASURES: Difference between time to recovery of CCF and time to recovery of functional object use (FOU) or functional communication (FC), the 2 existing criteria for eMCS as measured by the Coma Recovery Scale–Revised (CRS-R). RESULTS: Of 214 patients (median age, 53 years [interquartile range {IQR}, 34-66 years], male: 134 [62.6%], traumatic etiology: 115 [53.7%], admission CRS-R total score: 10 [IQR, 7-13]) admitted to rehabilitation without CCF, FOU, or FC, 162 (75.7%) recovered CCF and FOU or FC during the 8-week observation period. On average, recovery of CCF, FOU, and FC was observed within 1 day of one another, approximately 46 days (IQR, 38.25-58 days) post injury. One hundred and sixteen patients (71.6%) recovered FOU or FC prior to or at the same time as CCF. CONCLUSIONS: In patients recovering from DoC, CCF reemerges around the same time as FOU and FC. This finding may reflect the shared dependency of these behaviors on cognitive processes (eg, language comprehension, attention, motor control) that are essential for effective interpersonal interaction and social participation. Our results support the addition of CCF to the existing diagnostic criteria for eMCS, but further validation in an independent sample should be conducted.

Tools assaying the neural networks that modulate consciousness may facilitate tracking of recovery after acute severe brain injury. The ABCD framework classifies resting-state EEG into categories reflecting levels of thalamocortical network function that correlate with outcome in post-cardiac arrest coma. In this longitudinal cohort study, we applied the ABCD framework to 20 patients with acute severe traumatic brain injury requiring intensive care (12 of whom were also studied at ≥6-months post-injury) and 16 healthy controls. We tested four hypotheses: 1) EEG ABCD classifications are spatially heterogeneous and temporally variable; 2) ABCD classifications improve longitudinally, commensurate with the degree of behavioral recovery; 3) ABCD classifications correlate with behavioral level of consciousness; and 4) the Coma Recovery Scale-Revised arousal facilitation protocol yields improved ABCD classifications. Channel-level EEG power spectra were classified based on spectral peaks within pre-defined frequency bands: 'A' = no peaks above delta (4 Hz) range (complete thalamocortical disruption); 'B' = theta (4-8 Hz) peak (severe thalamocortical disruption); 'C' = theta and beta (13-24 Hz) peaks (moderate thalamocortical disruption); or 'D' = alpha (8-13 Hz) and beta peaks (normal thalamocortical function). Acutely, 95% of patients demonstrated 'D' signals in at least one channel but exhibited within-session temporal variability and spatial heterogeneity in the proportion of different channel-level ABCD classifications. By contrast, healthy participants and patients at follow-up consistently demonstrated signals corresponding to intact thalamocortical network function. Patients demonstrated longitudinal improvement in ABCD classifications (p .05) and ABCD classification distinguished patients with and without command-following in the subacute-to-chronic phase of recovery (p .01). In patients studied acutely, ABCD classifications improved after the Coma Recovery Scale-Revised arousal facilitation protocol (p .05) but did not correspond with behavioral level of consciousness. These findings support the use of the ABCD framework to characterize channel-level EEG dynamics and track fluctuations in functional thalamocortical network integrity in spatial detail.

Neuroprognostication following diffuse axonal injury (DAI) has historically relied on neuroimaging techniques with lower spatial resolution and contrast than techniques currently available in clinical practice. Since the initial studies of DAI classification and prognosis in the 1980s and 1990s, advances in neuroimaging have improved detection of brainstem microbleeds, a hallmark feature of Grade 3 DAI that has traditionally been associated with poor neurologic outcome. Here, we report clinical and radiologic data from two patients with severe traumatic brain injury and grade 3 DAI who recovered functional independence and returned to work within 4 months of injury. Importantly, both patients were scanned using 3 Tesla MRI protocols that included susceptibility-weighted imaging (SWI), a technique that provides enhanced sensitivity for detecting brainstem microbleeds. These observations highlight the importance of developing approaches to DAI classification and prognosis that better align with contemporary neuroimaging capabilities.

Traumatic brain injury (TBI) results in disparate outcomes ranging from persistent disorders of consciousness to symptom resolution. Despite the breadth and complexity of TBI recovery, most clinical trials dichotomize outcome by establishing an arbitrary cut-point, above and below which recovery is described as “favorable” and “unfavorable,” respectively. For example, the widely used eight-level Glasgow Outcome Scale-Extended (GOSE) is typically collapsed into these two categories. Dichotomizing the GOSE into “favorable” and “unfavorable” outcome may limit detection of treatment effects in TBI clinical trials, contribute to imprecise prognostic counseling, and unduly influence decision-making with regard to withdrawal of life-sustaining therapy. We illustrate the lack of standardization in defining “unfavorable” and “favorable” TBI outcome on the GOSE by identifying the broad range of cut-points, from a score of 3 (part-time supervision in the home required) to 7 (presence of some residual of symptoms), that have been used to dichotomize the GOSE. We also highlight the ethical concerns related to characterizing TBI outcomes solely from the perspective of investigators and clinicians, rather than patients and caregivers. Finally, we suggest that a pragmatic, immediate solution to GOSE dichotomization is to report the likelihood of achieving each of the eight GOSE outcome levels and propose a study design for a new patient- and caregiver-centered TBI outcome metric.

Accurately predicting emergence from disorders of consciousness (DoC) after acute brain injury can profoundly influence mortality, acute management, and rehabilitation planning. While recent advances in functional neuroimaging and stimulus-based EEG offer the potential to enrich shared decision-making, their procedural sophistication and expense limit widespread availability or repeated performance. We investigated continuous EEG (cEEG) within a passive, “resting-state” framework to provide continuously updated predictions of DoC recovery at 24-, 48-, and 72-hour prediction horizons. To develop robust, continuous prediction models from a large population of patients with acute brain injury (ABI), we leveraged a recently described pragmatic approach transforming Glasgow Coma Scale assessment sub-score combinations into frequently assessed DoC diagnoses: coma, vegetative state, minimally conscious state with or without language, and post-injury confusional or recovered states. We retrospectively identified consecutive patients undergoing cEEG following acute traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), or intracerebral hemorrhage (ICH). Models continuously predicting DoC diagnosis for multiple prediction horizons were evaluated utilizing recent clinical assessments with or without cEEG information, which comprised a comprehensive EEG feature set of 288 time, frequency, and time-frequency characteristics computed from consecutive 5-minute EEG epochs, with 6 additional features capturing each EEG feature’s temporal dynamics. Features were fed into a predictive model developed with cross-validation; the ordinal DoC diagnosis was discriminated using an ensemble of XGBoost binary classifiers. For 201 ABI patients (46 TBI, 140 SAH, 15 ICH patients comprising 27,280 cEEG-hours with concomitant clinical assessments), cEEG-augmented models accurately predicted the future DoC diagnosis at 24 hours (one-vs-rest AU-ROC, 92.4%; weighted-F1 84.1%), 48 hours (one-vs-rest AU-ROC=88%, weighted-F1=80%) and 72 hours (one-vs-rest AU-ROC=86.3%, weighted-F1=76.6%). Models were robust to utilizing different ordinal cut-points for the DoC prediction target and evaluating additional models derived from specific sub-populations using a confound-isolating cross-validation framework. The most robust features across evaluation configurations included Petrosian fractal dimension, relative power of high to low (gamma-beta to delta-alpha) EEG frequency spectra, energy within the 12-35 Hz frequency band in the short-time Fourier transform domain, and wavelet entropy. The cEEG-augmented model exceeded the performance of models using preceding clinical assessments, continuously predicting future DoC diagnosis with one-vs-rest AU-ROC in the range of 84.3-92.4% while utilizing approaches to limit overfitting. The proposed continuous, resting-state cEEG prediction method represents a promising tool to predict DoC emergence in ABI patients. Enabling these methods prospectively would represent a new paradigm of continuous prognostic monitoring for predicting coma recovery and assessing treatment response.

The effects of traumatic brain injury (TBI) are difficult to measure in longitudinal cohort studies, because disparate pre-injury characteristics and injury mechanisms produce variable impairment profiles and recovery trajectories. In preparation for the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study, which followed patients with injuries ranging from uncomplicated mild TBI to coma, we designed a multi-dimensional Flexible outcome Assessment Battery (FAB). The FAB relies on a decision-making algorithm that assigns participants to a Comprehensive (CAB) or Abbreviated Assessment Battery (AAB) and guides test selection across all phases of recovery. To assess feasibility of the FAB, we calculated the proportion of participants followed at 2 weeks (2w) and at 3, 6, and 12 months (3m, 6m, 12m) post-injury who completed the FAB and received valid scores. We evaluated utility of the FAB by examining differences in 6m and 12m Glasgow Outcome Scale-Extended (GOSE) scores between participant subgroups derived from the FAB-enabled versus traditional approach to outcome assessment applied at 2w. Among participants followed at 2w (

Emerging evidence suggests that repeated blast exposure (RBE) is associated with brain injury in military personnel. United States (U.S.) Special Operations Forces (SOF) personnel experience high rates of blast exposure during training and combat, but the effects of low-level RBE on brain structure and function in SOF have not been comprehensively characterized. Further, the pathophysiological link between RBE-related brain injuries and cognitive, behavioral, and physical symptoms has not been fully elucidated. We present a protocol for an observational pilot study, Long-Term Effects of Repeated Blast Exposure in U.S. SOF Personnel (ReBlast). In this exploratory study, 30 active-duty SOF personnel with RBE will participate in a comprehensive evaluation of: 1) brain network structure and function using Connectome magnetic resonance imaging (MRI) and 7 Tesla MRI; 2) neuroinflammation and tau deposition using positron emission tomography; 3) blood proteomics and metabolomics; 4) behavioral and physical symptoms using self-report measures; and 5) cognition using a battery of conventional and digitized assessments designed to detect subtle deficits in otherwise high-performing individuals. We will identify clinical, neuroimaging, and blood-based phenotypes that are associated with level of RBE, as measured by the Generalized Blast Exposure Value. Candidate biomarkers of RBE-related brain injury will inform the design of a subsequent study that will test a diagnostic assessment battery for detecting RBE-related brain injury. Ultimately, we anticipate that the ReBlast study will facilitate the development of interventions to optimize the brain health, quality of life, and battle readiness of U.S. SOF personnel.

Resting-state functional MRI is being used to develop diagnostic, prognostic and therapeutic biomarkers for critically ill patients with severe brain injuries. In studies of healthy volunteers and non-critically ill patients, prospective cardiorespiratory data are routinely collected to remove non-neuronal fluctuations in the resting-state functional MRI signal during analysis. However, the feasibility and utility of collecting cardiorespiratory data in critically ill patients on a clinical MRI scanner are unknown. We concurrently acquired resting-state functional MRI (repetition time = 1250 ms) and cardiac and respiratory data in 23 critically ill patients with acute severe traumatic brain injury and in 12 healthy control subjects. We compared the functional connectivity results from two approaches that are commonly used to correct cardiorespiratory noise: (i) denoising with cardiorespiratory data (i.e. image-based method for retrospective correction of physiological motion effects in functional MRI) and (ii) standard bandpass filtering. Resting-state functional MRI data in 7 patients could not be analysed due to imaging artefacts. In 6 of the remaining 16 patients (37.5%), cardiorespiratory data were either incomplete or corrupted. In patients (n = 10) and control subjects (n = 10), the functional connectivity results corrected with the image-based method for retrospective correction of physiological motion effects in functional MRI did not significantly differ from those corrected with bandpass filtering of 0.008–0.125 Hz. Collectively, these findings suggest that, in critically ill patients with severe traumatic brain injury, there is limited feasibility and utility to denoising the resting-state functional MRI signal with prospectively acquired cardiorespiratory data.

New techniques for individualized assessment of white matter integrity are needed to detect traumatic axonal injury (TAI) and predict outcomes in critically ill patients with acute severe traumatic brain injury (TBI). Diffusion MRI tractography has the potential to quantify white matter microstructure in vivo and has been used to characterize tract-specific changes following TBI. However, tractography is not routinely used in the clinical setting to assess the extent of TAI, in part because focal lesions reduce the robustness of automated methods. Here, we propose a pipeline that combines automated tractography reconstructions of 40 white matter tracts with multivariate analysis of along-tract diffusion metrics to assess the presence of TAI in individual patients with acute severe TBI. We used the Mahalanobis distance to identify abnormal white matter tracts in each of 18 patients with acute severe TBI as compared to 33 healthy subjects. In all patients for which a FreeSurfer anatomical segmentation could be obtained (17 of 18 patients), including 13 with focal lesions, the automated pipeline successfully reconstructed a mean of 37.5 +/- 2.1 white matter tracts without the need for manual intervention. A mean of 2.5 +/- 2.1 tracts resulted in partial or failed reconstructions and needed to be reinitialized upon visual inspection. The pipeline detected at least one abnormal tract in all patients (mean: 9.07 +/- 7.91) and could accurately discriminate between patients and controls (AUC: 0.91). The individual patients’ profiles showed the number and neuroanatomic location of abnormal tracts varied across patients and levels of consciousness. The premotor, temporal, and parietal sections of the corpus callosum were the most commonly damaged tracts (in 10, 9, and 8 patients respectively), consistent with histological studies of TAI. TAI measures were not associated with concurrent behavioral measures of consciousness. In summary, we provide proof-of-principle evidence that an automated tractography pipeline has translational potential to detect and quantify TAI in individual patients with acute severe traumatic brain injury.

As the clinical trial landscape for patients with disorders of consciousness (DoC) expands, consideration of associated ethical challenges and opportunities is of ever-increasing importance. Responsible conduct of research in the vulnerable population of persons with DoC, including those with coma, vegetative state/unresponsive wakefulness syndrome (VS/UWS), minimally conscious state (MCS), covert cortical processing (CCP), and cognitive motor dissociation (CMD), demands proactive deliberation of unique ethical issues that may arise and the adoption of robust protections to safeguard patients, surrogates, and other key stakeholders. Here we identify and critically evaluate four central categories of ethical considerations in clinical trials involving participants with DoC: (1) autonomy, respect for persons and informed consent of individuals with liminal consciousness; (2) balancing unknown benefits and risks, especially considering the epistemological gap between behavior and consciousness that complicates ordinary ascription of subjective states; (3) disclosure to surrogates and clinical teams of investigational results pertaining to consciousness; and (4) justice considerations, including equitable access to clinical trial enrollment across communities and geographies. We outline guiding principles and research opportunities for clinicians, neuroethicists, and researchers engaged in DoC clinical trials to advance ethical study design and deployment in this complex yet crucial area of investigation.