Samuel B. Snider’s research while affiliated with Brigham and Women's Hospital and other places

Accumulating evidence of heterogeneous long-term outcomes after traumatic brain injury (TBI) has challenged longstanding approaches to TBI outcome classification that are largely based on global functioning. A lack of studies with clinical and biomarker data from individuals living with chronic (>1 year post-injury) TBI has precluded refinement of long-term outcome classification ontology. Multimodal data in well-characterized TBI cohorts is required to understand the clinical phenotypes and biological underpinnings of persistent symptoms in the chronic phase of TBI. The present cross-sectional study leveraged data from 281 participants with chronic complicated mild-to-severe TBI in the Late Effects of Traumatic Brain Injury Study. Our primary objective was to develop and validate clinical phenotypes using data from 41 TBI measures spanning a comprehensive cognitive battery, motor testing, and assessments of mood, health, and functioning. We performed a 70/30% split of training (n=195) and validation (n=86) datasets and performed principal components analysis to reduce the dimensionality of data. We used Hierarchical Clustering on Principal Components with k-means consolidation to identify clusters, or phenotypes, with shared clinical features. Our secondary objective was to investigate differences in brain volume in seven cortical networks across clinical phenotypes in the subset of 168 participants with brain MRI data. We performed multivariable linear regression models adjusted for age, age-squared, sex, scanner, injury chronicity, injury severity, and training/validation set. In the training/validation sets, we observed four phenotypes: 1) mixed cognitive and mood/behavioral deficits (11.8%; 15.1% in the training and validation set, respectively); 2) predominant cognitive deficits (20.5%; 23.3%); 3) predominant mood/behavioral deficits (27.7%; 22.1%); and 4) few deficits across domains (40%; 39.5%). The predominant cognitive deficit phenotype had lower cortical volumes in executive control, dorsal attention, limbic, default mode, and visual networks, relative to the phenotype with few deficits. The predominant mood/behavioral deficit phenotype had lower volumes in dorsal attention, limbic, and visual networks, compared to the phenotype with few deficits. Contrary to expectation, we did not detect differences in network-specific volumes between the phenotypes with mixed deficits versus few deficits. We identified four clinical phenotypes and their neuroanatomic correlates in a well-characterized cohort of individuals with chronic TBI. Phenotypes defined by symptom clusters, as opposed to global functioning, could inform clinical trial stratification. Individuals with predominant cognitive and mood/behavioral deficits had reduced cortical volumes in specific cortical networks, providing insights into sensitive, though not specific, candidate imaging biomarkers of clinical symptom phenotypes after chronic TBI and potential targets for intervention.

Accumulating evidence of heterogeneous long-term outcomes after traumatic brain injury (TBI) has challenged longstanding approaches to TBI outcome classification that are largely based on global functioning. A lack of studies with clinical and biomarker data from individuals living with chronic (>1 year post-injury) TBI has precluded refinement of long-term outcome classification ontology. Multimodal data in well-characterized TBI cohorts is required to understand the clinical phenotypes and biological underpinnings of persistent symptoms in the chronic phase of TBI. The present cross-sectional study leveraged data from 281 participants with chronic complicated mild-to-severe TBI in the Late Effects of Traumatic Brain Injury (LETBI) Study. Our primary objective was to develop and validate clinical phenotypes using data from 41 TBI measures spanning a comprehensive cognitive battery, motor testing, and assessments of mood, health, and functioning. We performed a 70/30% split of training (n=195) and validation (n=86) datasets and performed principal components analysis to reduce the dimensionality of data. We used Hierarchical Cluster Analysis on Principal Components with k-means consolidation to identify clusters, or phenotypes, with shared clinical features. Our secondary objective was to investigate differences in brain volume in seven cortical networks across clinical phenotypes in the subset of 168 participants with brain MRI data. We performed multivariable linear regression models adjusted for age, age-squared, sex, scanner, injury chronicity, injury severity, and training/validation set. In the training/validation sets, we observed four phenotypes: 1) mixed cognitive and mood/behavioral deficits (11.8%; 15.1% in the training and validation set, respectively); 2) predominant cognitive deficits (20.5%; 23.3%); 3) predominant mood/behavioral deficits (27.7%; 22.1%); and 4) few deficits across domains (40%; 39.5%). The predominant cognitive deficit phenotype had lower cortical volumes in executive control, dorsal attention, limbic, default mode, and visual networks, relative to the phenotype with few deficits. The predominant mood/behavioral deficit phenotype had lower volumes in dorsal attention, limbic, and visual networks, compared to the phenotype with few deficits. Contrary to expectation, we did not detect differences in network-specific volumes between the phenotypes with mixed deficits versus few deficits. We identified four clinical phenotypes and their neuroanatomic correlates in a well-characterized cohort of individuals with chronic TBI. TBI phenotypes defined by symptom clusters, as opposed to global functioning, could inform clinical trial stratification and treatment selection. Individuals with predominant cognitive and mood/behavioral deficits had reduced cortical volumes in specific cortical networks, providing insights into sensitive, though not specific, candidate imaging biomarkers of clinical symptom phenotypes after chronic TBI and potential targets for intervention.

Introduction Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. We sought to determine whether internal EEG subparameters extracted by the Bispectral Index (BIS) monitor, a device commonly used to estimate depth-of-anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest. Methods In this retrospective cohort study, we trained a 3-layer neural network to predict recovery of consciousness to the point of command following versus not based on 48 hours of continuous EEG recordings in 315 comatose patients admitted to a single US academic medical center after cardiac arrest (Derivation cohort: N=181; Validation cohort: N=134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine (i.e., the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. Our model was trained on hourly-averaged measurements of these internal subparameters. We compared this model’s performance to the modified Westhall qualitative EEG scoring framework. Results Maximum prognostic accuracy in the Derivation Cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, our model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristic curve: 0.86, accuracy: 0.87, sensitivity: 0.83, specificity: 0.88, positive predictive value: 0.71, negative predictive value: 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest. Conclusions In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative gold-standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

This cohort study examines the association of time to command-following with death or dependency at 1 year among individuals with moderate-severe traumatic brain injury (TBI).

Cognitive impairment, often due to attentional deficits, is a primary driver of disability after traumatic brain injury. It remains unclear whether attentional deficits are caused by injury to specific brain structures or the total burden of injury. In this cross-sectional, multicentre cohort study, we tested whether the association between brain injury and attentional performance varies by neuroanatomic location. Participants in the late effects of traumatic brain injury study were at least 18 years old and at least 1 year after a mild, moderate or severe traumatic brain injury. They underwent MRI and neuropsychological assessment at one of two sites. The primary and secondary outcomes, each measuring aspects of attentional performance, were the Trails A t-score and the standardized score on California Verbal Learning Test 2 Immediate Recall Trial 1. Imaging variables included the size and location (seven regions and seven networks) of encephalomalacic brain lesions and regional white matter fractional anisotropy measured with diffusion MRI (14 regions). We used ANOVA to test whether attentional performance differed by lesion location and linear mixed models to test whether attentional performance differed based on regional fractional anisotropy. One hundred eighty-eight participants met inclusion criteria (mean age 57, 69% male, 88% White). Participants with encephalomalacic brain lesions [N = 73 (39%)] had worse Trails A [mean (95% confidence interval) difference: 4.7 (0.3, 9.1); P = 0.036] but not secondary outcome performance [−0.3 (−0.1, 0.7); P = 0.17]. Among participants with lesions, Trails A performance did not differ by lesion size (P = 0.07) or location (P = 0.41 by region; P = 0.78 by network). We identified a significant interaction between regional fractional anisotropy and attentional performance on both primary (P = 0.001) and secondary (P = 0.001) outcome measures. Post hoc testing identified the strongest associations with Trails A performance in the sagittal stratum [1 SD decrement in Trails A: −0.2 (−0.3, −0.1) SD change in fractional anisotropy; PBonferroni = 0.0057] and external capsule [−0.1 (−0.2, −0.1); PBonferroni = 0.042] and the strongest association with secondary attentional scores in the corpus callosum [0.2 (0.1, 0.3); PBonferroni = 0.014]. In a multivariate model, white matter integrity in the sagittal stratum (P = 0.008), but not encephalomalacic lesions (P = 0.3), was independently associated with Trails A performance. Diminished white matter integrity and cortical injury were each associated with attentional test performance, but only white matter injury demonstrated independent and region-specific effects. The peak statistical association with attentional test performance was in the sagittal stratum, a widely connected white matter region. Further investigation into the connections spanning this and nearby regions may reveal therapeutic targets for neuromodulation.

Disorders of consciousness (DoC) are states of impaired arousal or awareness. Deep brain stimulation (DBS) is a potential treatment, but outcomes vary, possibly due to differences in patient characteristics, electrode placement, or stimulation of specific brain networks. We studied 40 patients with DoC who underwent DBS targeting the thalamic centromedian-parafascicular complex. Better-preserved gray matter, especially in the striatum, correlated with consciousness improvement. Stimulation was most effective when electric fields extended into parafascicular and subparafascicular nuclei—ventral to the centromedian nucleus, near the midbrain— and when it engaged projection pathways of the ascending arousal network, including the hypothalamus, brainstem, and frontal lobe. Moreover, effective DBS sites were connected to networks similar to those underlying impaired consciousness due to generalized absence seizures and acquired lesions. These findings support the therapeutic potential of DBS for DoC, emphasizing the importance of precise targeting and revealing a broader link between effective DoC treatment and mechanisms underlying other conscciousness-impairing conditions.

Importance Because withdrawal of life-sustaining therapy based on perceived poor prognosis is the most common cause of death after moderate or severe traumatic brain injury (TBI), the accuracy of clinical prognoses is directly associated with mortality. Although the location of brain injury is known to be important for determining recovery potential after TBI, the best available prognostic models, such as the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) score, do not currently incorporate brain injury location. Objective To test whether automated measurement of cerebral hemorrhagic contusion size and location is associated with improved prognostic performance of the IMPACT score. Design, Setting, and Participants This prognostic cohort study was performed in 18 US level 1 trauma centers between February 26, 2014, and August 8, 2018. Adult participants aged 17 years or older from the US-based Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study with moderate or severe TBI (Glasgow Coma Scale score 3-12) and contusions detected on brain computed tomography (CT) scans were included. The data analysis was performed between January 2023 and February 2024. Exposures Labeled contusions detected on CT scans using Brain Lesion Analysis and Segmentation Tool for Computed Tomography (BLAST-CT), a validated artificial intelligence algorithm. Main Outcome and Measure The primary outcome was a Glasgow Outcome Scale–Extended (GOSE) score of 4 or less at 6 months after injury. Whether frontal or temporal lobe contusion volumes improved the performance of the IMPACT score was tested using logistic regression and area under the receiver operating characteristic curve comparisons. Sparse canonical correlation analysis was used to generate a disability heat map to visualize the strongest brainwide associations with outcomes. Results The cohort included 291 patients with moderate or severe TBI and contusions (mean [SD] age, 42 [18] years; 221 [76%] male; median [IQR] emergency department arrival Glasgow Coma Scale score, 5 [3-10]). Only temporal contusion volumes improved the discrimination of the IMPACT score (area under the receiver operating characteristic curve, 0.86 vs 0.84; P = .03). The data-derived disability heat map of contusion locations showed that the strongest association with unfavorable outcomes was within the bilateral temporal and medial frontal lobes. Conclusions and Relevance These findings suggest that CT-based automated contusion measurement may be an immediately translatable strategy for improving TBI prognostic models.

Although magnetic resonance imaging, particularly diffusion-weighted imaging, has increasingly been used as part of a multimodal approach to prognostication in patients who are comatose after cardiac arrest, the performance of quantitative analysis of apparent diffusion coefficient (ADC) maps, as compared to standard radiologist impression, has not been well characterized. This retrospective study evaluated quantitative ADC analysis to the identification of anoxic brain injury by diffusion abnormalities on standard clinical magnetic resonance imaging reports. The cohort included 204 previously described comatose patients after cardiac arrest. Clinical outcome was assessed by (1) 3–6 month post-cardiac-arrest cerebral performance category and (2) coma recovery to following commands. Radiological evaluation was obtained from clinical reports and characterized as diffuse, cortex only, deep gray matter structures only, or no anoxic injury. Quantitative analyses of ADC maps were obtained in specific regions of interest (ROIs), whole cortex, and whole brain. A subgroup analysis of 172 was performed after eliminating images with artifacts and preexisting lesions. Radiological assessment outperformed quantitative assessment over all evaluated regions (area under the curve [AUC] 0.80 for radiological interpretation and 0.70 for the occipital region, the best performing ROI, p = 0.011); agreement was substantial for all regions. Radiological assessment still outperformed quantitative analysis in the subgroup analysis, though by smaller margins and with substantial to near-perfect agreement. When assessing for coma recovery only, the difference was no longer significant (AUC 0.83 vs. 0.81 for the occipital region, p = 0.70). Although quantitative analysis eliminates interrater differences in the interpretation of abnormal diffusion imaging and avoids bias from other prediction modalities, clinical radiologist interpretation has a higher predictive value for outcome. Agreement between radiological and quantitative analysis improved when using high-quality scans and when assessing for coma recovery using following commands. Quantitative assessment may thus be more subject to variability in both clinical management and scan quality than radiological assessment.

Identical bursts on electroencephalography (EEG) are considered a specific predictor of poor outcomes in cardiac arrest, but its relationship with structural brain injury severity on magnetic resonance imaging (MRI) is not known. This was a retrospective analysis of clinical, EEG, and MRI data from adult comatose patients after cardiac arrest. Burst similarity in first 72 h from the time of return of spontaneous circulation were calculated using dynamic time-warping (DTW) for bursts of equal (i.e., 500 ms) and varying (i.e., 100–500 ms) lengths and cross-correlation for bursts of equal lengths. Structural brain injury severity was measured using whole brain mean apparent diffusion coefficient (ADC) on MRI. Pearson’s correlation coefficients were calculated between mean burst similarity across consecutive 12–24-h time blocks and mean whole brain ADC values. Good outcome was defined as Cerebral Performance Category of 1–2 (i.e., independence for activities of daily living) at the time of hospital discharge. Of 113 patients with cardiac arrest, 45 patients had burst suppression (mean cardiac arrest to MRI time 4.3 days). Three study participants with burst suppression had a good outcome. Burst similarity calculated using DTW with bursts of varying lengths was correlated with mean ADC value in the first 36 h after cardiac arrest: Pearson’s r: 0–12 h: − 0.69 (p = 0.039), 12–24 h: − 0.54 (p = 0.002), 24–36 h: − 0.41 (p = 0.049). Burst similarity measured with bursts of equal lengths was not associated with mean ADC value with cross-correlation or DTW, except for DTW at 60–72 h (− 0.96, p = 0.04). Burst similarity on EEG after cardiac arrest may be associated with acute brain injury severity on MRI. This association was time dependent when measured using DTW.