Stroke is a leading cause of long-term disability, with most survivors showing upper limb motor impairment. Spasticity is a common outcome, marked by excessive muscle tone during quick joint extension. This can reduce range of motion, limit functional arm use, and cause pain. Despite the prominence of spasticity, its relationship to motor recovery and underlying neural mechanisms remains unclear. In this longitudinal study, we examine how post-stroke spasticity relates to upper limb motor recovery and investigate associated patterns of neuroanatomical injury (both lesions and networks). Methods: We recruited 59 acute stroke patients in the first week after stroke, all of whom underwent MR neuroimaging. Patients returned at 3 months to have upper limb spasticity measured with the Modified Ashworth Scale (MAS). Both times, upper limb motor impairment was measured with the Fugl-Meyer scale (FMA). We used (a) Voxel Lesion Symptom Mapping (VLSM) to link lesion location to presence of spasticity (MAS≥1) with and without FMA as a covariate and (b) Lesion Network Mapping (LNM) to quantify patients’ lesion connectivity in order to estimate spasticity-related disruptions of functional brain networks. Results: Patients showed recovery of motor impairment from acute (FMA: 31.1±3.1, mean±SEM) to subacute phase (FMA: 43.9±2.9). More severe impairment (lower FMA) in first week after stroke predicted development of spasticity (MAS≥1) at 3 months (Spearman’s ρ=-0.6, p=4.3×10 -7 , Fig.1a). Patients with spasticity at 3 months showed markedly less recovery of FMA (Fig.1b). Voxels associated with both spasticity and motor impairment centered on the corticospinal tract (CST); voxels uniquely associated with spasticity centered in basal ganglia (Fig.2). Lesions in patients with spasticity (vs. without) were connected to ext. capsule, operculum, and orbitofrontal cortex (OFC) (Fig.3). Discussion: In the first 3 months after stroke, the development of upper limb spasticity is associated with worse motor impairment and reduced recovery. The effect of CST damage on spasticity is compounded by lesions to basal ganglia. LNM associated spasticity with networks including OFC. Developing spasticity after acute stroke is thus associated with direct damage to CST but also damage to and connectivity with broader neuromodulatory networks including basal ganglia and OFC. Findings open the door to targeted neurorehabilitation, e.g. neuromodulation, to improve specific components of hemiparesis.

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.

AIM:To correlate brain metabolites with clinical outcomes using magnetic resonance spectroscopy (MRS) in cardiac arrest (CA) patients and assess their prognostic performance compared to quantitative apparent diffusion coefficient (ADC) maps. METHODS:Comatose CA patients who underwent MRI and concurrent MRS were included. PRIMARY OUTCOME:coma recovery at hospital discharge; secondary outcome: good neurological function at 6 months (Cerebral Performance Index 1-2, vs. 3-5). Six metabolites were measured in the posterior cingulate gyrus (PCG), parietal white matter, and brainstem. Mean ADC values, and percentage of voxels with ADC <450 and <650 × 10-6 × mm2/s were computed for whole brain and specific regions. Prognostic performance was compared using Receiver Operating Characteristic (ROC) curves. RESULTS:Of 94 patients, 25 (27 %) achieved coma recovery, and 22 (23 %) had a good outcome at 6 months. N-acetylaspartate/Creatine (NAA/Cr) in the PCG was most discriminative for coma recovery (median 1.29, IQR 0.21 vs. 0.86, 0.32; p-value <0.0001). NAA/Cr had the highest area under the curve for coma recovery (0.9, 95 % CI 0.84-0.96) and good outcome at 6 months (AUROC 0.88, 95 % CI 0.82-0.95), significantly outperforming all quantitative ADC measurements, except mean ADC of the PCG for the secondary outcome (adj. p-value = 0.064). Multivariable models incorporating NAA/Cr or ADC, alongside clinical and EEG variables, demonstrated improved performance compared to models with clinical and EEG variables alone, though the difference was not statistically significant. Adding MRS to established early predictors of favorable outcome increased the specificity from 67 % to 93 % at 100 % sensitivity. CONCLUSION:MRS-derived NAA/Cr in the PCG is a valuable predictor of good outcome in comatose CA patients, outperforming quantitative ADC measurements for coma recovery. Further studies are needed to optimize MRS acquisition for multimodal neuroprognostication.

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. Snider et al. report that cortical injury and diminished white matter integrity were each associated with attentional impairment in patients with chronic traumatic brain injury. Region-specific effects were only observed in the white matter. The strongest association with attentional impairment was identified in the sagittal stratum, a widely connected region.

Disorders of consciousness are characterized by severe impairments in arousal and awareness. Deep brain stimulation is a potential treatment, but outcomes vary-possibly due to differences in patient characteristics, electrode placement, or the specific brain network engaged. We describe 40 patients with disorders of consciousness undergoing deep brain stimulation targeting the thalamic centromedian-parafascicular complex. Improvements in consciousness are associated with better-preserved gray matter, particularly in the striatum. Electric field modeling reveals that stimulation is most effective when it extends below the centromedian nucleus, engaging the inferior parafascicular nucleus and the adjacent ventral tegmental tract-a pathway that connects the brainstem and hypothalamus and runs along the midbrain-thalamus border. External validation analyzed show that effective stimulation engages a brain network overlapping with disrupted patterns of brain activity observed in two independent cohorts with impaired consciousness: one with arousal-impairing stroke lesions and the other with awareness-impairing seizures. Together, these findings advance the field by informing patient selection, refining stimulation targets, and identifying a brain network linked to recovery that may have broader therapeutic relevance across consciousness-impairing conditions.

Background: Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. The authors sought to determine whether internal electroencephalogram (EEG) subparameters extracted by the BIS monitor (Medtronic, USA), 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, a three-layer neural network was trained to predict recovery of consciousness to the point of command following versus not based on 48 h of continuous EEG recordings in 315 comatose patients admitted to a single U.S. 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. The model was trained on hourly averaged measurements of these internal subparameters. This model’s performance was compared 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, the model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristics 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 accepted standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

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 are 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: (i) mixed cognitive and mood/behavioural deficits (11.8%; 15.1% in the training and validation sets, respectively); (ii) predominant cognitive deficits (20.5%; 23.3%); (iii) predominant mood/behavioural deficits (27.7%; 22.1%); and (iv) 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/behavioural 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/behavioural 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.

Traumatic brain injury (TBI) is a risk factor for neurodegeneration and cognitive decline, yet the underlying pathophysiologic mechanisms are incompletely understood. This gap in knowledge is in part related to a lack of reliable and efficient methods for measuring cortical lesions in neuroimaging studies. The objective of this study was to develop a semi-automated lesion detection tool and apply it to an investigation of longitudinal changes in brain structure among individuals with chronic TBI. We identified 24 individuals with chronic moderate-to-severe TBI enrolled in the Late Effects of TBI (LETBI) study who had cortical lesions detected by T1-weighted MRI and underwent two MRI scans at least two years apart. Initial MRI scans were performed more than one year post-injury, and follow-up scans were performed 3.1 (IQR=1.7) years later. We leveraged FreeSurfer parcellations of T1-weighted MRI volumes and a recently developed super-resolution technique, SynthSR, to automate the identification of cortical lesions in this longitudinal dataset. Trained raters received the data in a randomized order and manually edited the automated lesion segmentations, yielding a final semi-automated lesion mask for each scan at each time point. Inter-rater variability was assessed in an independent cohort of 10 additional LETBI subjects with cortical lesions. The semi-automated lesion segmentations showed a high level of accuracy compared to "ground truth" lesion segmentations performed via manual segmentation by a separate blinded rater. In a longitudinal analysis of the semi-automated segmentations, lesion volume increased between the two time points with a median volume change of 4.91 (IQR=12.95) mL (p<0.0001). Lesion volume significantly expanded in 40 of 61 measured lesions (65.6%), as defined by a longitudinal volume increase that exceeded inter-rater variability. Longitudinal analyses showed similar changes in lesion volume using the ground-truth lesion segmentations. Inter-scan duration was not associated with the magnitude of lesion growth. Reliable and efficient semi-automated lesion segmentation is feasible in studies of chronic TBI, creating opportunities to elucidate mechanisms of post-traumatic neurodegeneration.

Importance Recovery of command-following after traumatic brain injury (TBI) is an important prognostic indicator, however, the relationship between time to command-following and long-term functional outcome is not clear. Objective Evaluate the association between command-following and outcome 1-year after TBI. Design Cohort study of participants with moderate-severe TBI in the TBI Model Systems (TBIMS) who were followed 1-year post injury, and validation in an independent dataset from the Brain Trauma Research Center (BTRC) database. Setting TBIMS is a multi-center study of participants with moderate-severe TBI treated in an inpatient rehabilitation hospital. The BTRC database is derived from a single US level 1 trauma center and includes patients with severe TBI. Participants TBIMS: N=9,052 (mean+/-SD age 38+/-18 years, 76% male, 67% white); BTRC: N=228 (mean age 37+/-17 years, 76% male, 91% white). Participants did not follow commands on acute hospital admission and survived to discharge. Exposure Days to command-following during hospitalization. Main Outcome Glasgow Outcome Scale Extended (GOSE) score <4 (i.e., death or dependency) 1-year post TBI. Results: Participants in TBIMS were more likely than those in BTRC to follow commands during acute hospitalization (90% vs 63%; p<0.001) and had a shorter median time to command-following (5 vs 9.5 days; p< 0.001). For each additional week without command-following, the odds ratio for death or dependency at 1 year was 1.30 (95% CI: [1.26,1.35]; p<0.001) in TBIMS and 1.49 ([1.15, 1.97]; p=0.003) in BTRC. Time to command-following had an AUC of 0.61 [0.59, 0.63] in TBIMS and 0.65 [0.53, 0.76]) in BTRC. Each additional day without command-following was associated with a 1.18% (1.16%, 1.20%) increase in the proportion of participants with death or dependency at 1-year in TBIMS and 1.05% (0.99%, 1.11%) in BTRC. Conclusion: Time to command-following after moderate-severe TBI is associated with 1-year outcomes, but the predictive accuracy of absence of command-following on any single post-injury day is limited. In two independent cohorts, the likelihood of death or dependency increased by ~1% for each additional day without command-following. Clinicians should be cautious when prognosticating based on the absence of command-following in the first five weeks after TBI.### Competing Interest StatementDr. Hammond receives royalties from Springer/Demos and Lash Publishing, and she has served on Advisory Boards for Avanir and Otsuka Pharmaceuticals. Dr. Zafonte reported receiving royalties from Springer/Demos for the text Brain Injury Medicine as well as serving on the scientific advisory boards of Myomo, and One Care.ai. Dr. Giacino occasionally receives honoraria from academic and medical institutions for conducting training seminars on the Coma Recovery Scale- Revised. ### Funding StatementDr. Snider receives funding from the National Institute of Neurologic Disorders and Stroke (1K23NS136767-01), American Heart Association, and National Institute of Biomedical Imaging and Bioengineering (1U01EB034228-01). Dr. Giacino receives funding from National Institutes of Health, National Institute of Neurologic Disorders and Stroke (U01 NS1365885; UG3NS117844-02; 5U01NS114140; 5UH3NS112826-04; 5R01NS102574-04; 5UH3NS095554-04), National Institute on Disability, Independent Living, and Rehabilitation Research (90DPCP0008; 90DPTB0011; 90DPHF0006; 90DPTB0027), and U.S. Department of Defense (W81XWH2210925; W81XWH-15-9-0001; W81XWH1910861). Dr. Hammond receives funding from National Institute on Disability, Independent Living, and Rehabilitation Research (grants 90DPTB0035, 90DPTB0022, 90DPTB0002, 90DPHF0006, 90DPTB0017, and 90RTEM0008); National Institutes of Health (UG3NS117844 and 1R01NS118009), PCORI UWSC9923/PCS-1604-35115; Department of Defense (W81XWH-18-1-0796); University of California- San Francisco; University of Michigan (SUBK10416CSPR-002). Dr. Kowalski receives funding from: NIH National Institute of Neurological Disorders and Stroke (LRP). Dr. Zafonte receives funding from National Institute on Disability, Independent Living and Rehabilitation Research (NIDILRR), Administration for Community Living (90DPCP0008-01-00, 90DP0039) Dr. Bodien receives funding from: NIH National Institute of Neurological Disorders and Stroke (U01 NS1365885, U01-NS086090), and the National Institute on Disability, Independent Living and Rehabilitation Research (NIDILRR), Administration for Community Living (90DPCP0008-01-00, 90DP0039). Dr. Walker receives funding from National Institute on Disability, Independent Living and Rehabilitation Research (NIDILRR), Administration for Community Living (90DBTB0021). ### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Institutional Review Boards at each TBIMS site and the BTRC approved the study, and participants surrogates provided informed consent.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors

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).

Consciousness is composed of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that underlie awareness in the human brain, but knowledge about the subcortical networks that sustain arousal in humans is incomplete. Here, we aimed to map the connectivity of a proposed subcortical arousal network that sustains wakefulness in the human brain, analogous to the cortical default mode network (DMN) that has been shown to contribute to awareness. We integrated data from ex vivo diffusion magnetic resonance imaging (MRI) of three human brains, obtained at autopsy from neurologically normal individuals, with immunohistochemical staining of subcortical brain sections. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain. Deterministic and probabilistic tractography analyses of the ex vivo diffusion MRI data revealed projection, association, and commissural pathways linking dAAN nodes with one another and with DMN nodes. Complementary analyses of in vivo 7-tesla resting-state functional MRI data from the Human Connectome Project identified the dopaminergic ventral tegmental area in the midbrain as a widely connected hub node at the nexus of the subcortical arousal and cortical awareness networks. Our network-based autopsy methods and connectivity data provide a putative neuroanatomic architecture for the integration of arousal and awareness in human consciousness.

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.

BACKGROUND AND PURPOSE:Thresholds for abnormal transcranial Doppler cerebrovascular reactivity (CVR) studies are poorly understood, especially for patients with cerebrovascular disease. Using a real-world cohort with cerebral arterial stenosis, we sought to describe a clinically significant threshold for carbon dioxide reactivity (CO2R) and vasomotor range (VMR). METHODS:CVR studies were performed during conditions of breathing room air normally, breathing 8% carbon dioxide air mixture, and hyperventilation. The mean and standard deviation (SD) of CO2R and VMR were calculated for the unaffected side in patients with unilateral stenosis; a deviation of 2 SDs below the mean was chosen as the threshold for abnormal. Receiver operating characteristic (ROC) curves for both sides for patients with unilateral and bilateral stenosis were evaluated for sensitivity (Sn) and specificity (Sp). RESULTS:A total of 133 consecutive CVR studies were performed on 62 patients with stenosis with mean±SD age 55±16 years. Comorbidities included hypertension (60%), diabetes (15%), stroke (40%), and smoking (35%). In patients with unilateral stenosis, mean±SD CO2R for the unaffected side was 1.86±0.53%, defining abnormal CO2R as <0.80%. Mean±SD CO2R for the affected side was 1.27±0.90%. The CO2R threshold predicted abnormal acetazolamide single-photon emission computed tomography (SPECT) (Sn = .73, Sp = .79), CT/MRI perfusion abnormality (Sn = .42, Sp = .77), infarction on MRI (Sn = .45, Sp = .76), and pressure-dependent exam (Sn = .50, Sp = .76). For the unaffected side, mean±SD VMR was 39.5±15.8%, defining abnormal VMR as <7.9%. For the affected side, mean±SD VMR was 26.5±17.8%. The VMR threshold predicted abnormal acetazolamide SPECT (Sn = .46, Sp = .94), infarction on MRI (Sn = .27, Sp = .94), and pressure-dependent exam (Sn = .31, Sp = .90). CONCLUSIONS:In patients with multiple vascular risk factors, a reasonable threshold for clinically significant abnormal CO2R is <0.80% and VMR is <7.9%. Noninvasive CVR may aid in diagnosing and risk stratifying patients with stenosis.

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.

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.

Transcranial magnetic stimulation (TMS) is effective for major depressive disorder (MDD) despite imprecise scalp-based targeting. Retrospective analyses suggest that targeting one brain circuit improves “dysphoric” symptoms, while targeting a different brain circuit improves “anxiosomatic” symptoms. Here, we tested this hypothesis prospectively. Individuals with moderate-to-severe MDD and moderate-to-severe anxiety (n=40) were randomized to receive TMS with dysphoric circuit targeting (dorsolateral prefrontal cortex) or anxiosomatic circuit targeting (dorsomedial prefrontal cortex). As hypothesized, dysphoric circuit targeting (n=16) improved BDI more than BAI (ratio 1.08, IQR 0.69-2.02), while anxiosomatic circuit targeting (n=20) improved BAI more than BDI (ratio 0.70, IQR 0.01-1.01) (Wilcoxon rank-sum test p=0.0195). This result was driven by larger improvements in anxiety with -anxiosomatic circuit targeting (p=0.0301). Thus, TMS targeting different brain circuits differentially modulates comorbid anxiety and depression symptoms. Symptom- and circuit-specific trial designs may improve power and better control for placebo and non-specific effects.