An Unpredictable Brain Is a Conscious, Responsive Brain.

Severe traumatic brain injuries typically result in loss of consciousness or coma. In deeply comatose patients with traumatic brain injury, cortical dynamics become simple, repetitive, and predictable. We review evidence that this low-complexity, high-predictability state results from a passive cortical state, represented by a stable repetitive attractor, that hinders the flexible formation of neuronal ensembles necessary for conscious experience. Our data and those from other groups support the hypothesis that this cortical passive state is because of the loss of thalamocortical input. We identify the unpredictability and complexity of cortical dynamics captured by local field potential as a sign of recovery from this passive coma attractor. In this Perspective article, we discuss how these electrophysiological biomarkers of the recovery of consciousness could inform the design of closed-loop stimulation paradigms to treat disorders of consciousness.

Brain-Computer Interfaces for Communication in Patients with Disorders of Consciousness: A Gap Analysis and Scientific Roadmap.

BACKGROUND: We developed a gap analysis that examines the role of brain-computer interfaces (BCI) in patients with disorders of consciousness (DoC), focusing on their assessment, establishment of communication, and engagement with their environment. METHODS: The Curing Coma Campaign convened a Coma Science work group that included 16 clinicians and neuroscientists with expertise in DoC. The work group met online biweekly and performed a gap analysis of the primary question. RESULTS: We outline a roadmap for assessing BCI readiness in patients with DoC and for advancing the use of BCI devices in patients with DoC. Additionally, we discuss preliminary studies that inform development of BCI solutions for communication and assessment of readiness for use of BCIs in DoC study participants. Special emphasis is placed on the challenges posed by the complex pathophysiologies caused by heterogeneous brain injuries and their impact on neuronal signaling. The differences between one-way and two-way communication are specifically considered. Possible implanted and noninvasive BCI solutions for acute and chronic DoC in adult and pediatric populations are also addressed. CONCLUSIONS: We identify clinical and technical gaps hindering the use of BCI in patients with DoC in each of these contexts and provide a roadmap for research aimed at improving communication for adults and children with DoC, spanning the clinical spectrum from intensive care unit to chronic care.

Thalamic amplification of cortical connectivity sustains attentional control.

Although interactions between the thalamus and cortex are critical for cognitive function, the exact contribution of the thalamus to these interactions remains unclear. Recent studies have shown diverse connectivity patterns across the thalamus, but whether this diversity translates to thalamic functions beyond relaying information to or between cortical regions is unknown. Here we show, by investigating the representation of two rules used to guide attention in the mouse prefrontal cortex (PFC), that the mediodorsal thalamus sustains these representations without relaying categorical information. Specifically, mediodorsal input amplifies local PFC connectivity, enabling rule-specific neural sequences to emerge and thereby maintain rule representations. Consistent with this notion, broadly enhancing PFC excitability diminishes rule specificity and behavioural performance, whereas enhancing mediodorsal excitability improves both. Overall, our results define a previously unknown principle in neuroscience; thalamic control of functional cortical connectivity. This function, which is dissociable from categorical information relay, indicates that the thalamus has a much broader role in cognition than previously thought.

Learning the hierarchical organization of the frontal lobe with differential causal effects.

In this video article, accompanying the paper "An approach to learning the hierarchical organization of the frontal lobe", we discuss a data driven approach to learning brain connectivity. Hierarchical models of brain connectivity are useful to understand how the brain can process sensory information, make decisions, and perform other high-level tasks. Despite extensive research, understanding the structure of the prefrontal cortex (PFC) remains a crucial challenge. In this work, we propose an approach to studying brain signals and uncovering characteristics of the underlying neural circuity, based on the mathematics of Gaussian processes and causal strengths. For discovering causations, we propose a metric referred to as double-averaged differential causal effect, which is a variant of the recently proposed differential causal effect, and it can be used as a principled measure of the causal strength between time series. We applied this methodology to study local field potential data from the frontal lobe, where the interest was in finding the causal relationship between the medial and lateral PFC areas of the brain. Our results suggest that the medial PFC causally influences the lateral PFC.

Primate thalamic nuclei select abstract rules and shape prefrontal dynamics.

Flexible behavior depends on abstract rules to generalize beyond specific instances, and outcome monitoring to adjust actions. Cortical circuits are posited to read out rules from high-dimensional representations of task-relevant variables in prefrontal cortex (PFC). We instead hypothesized that converging inputs from PFC, directly or via basal ganglia (BG), enable primate-specific thalamus to select rules. To test this, we simultaneously measured spiking activity across PFC and two connected thalamic nuclei of monkeys applying rules. Abstract rule information first appeared in the ventroanterior thalamus (VA) - the main thalamic hub between BG and PFC. The mediodorsal thalamus (MD) also represented rule information before PFC, which persisted after rule cues were removed, to help maintain activation of relevant posterior PFC cell ensembles. MD, a major recipient of midbrain dopamine input, was first to represent information about behavioral outcomes. This persisted after the trial (also in PFC). A PFC-BG-thalamus model reproduced key findings, and thalamic-lesion modeling disrupted PFC rule representations. These results suggest a revised view of the neural basis of flexible behavior in primates, featuring a central role for thalamus in selecting high-level cognitive information from PFC and implementing post-error behavioral adjustments, and of the functional organization of PFC along its anterior-posterior dimension.

Simulated resections and responsive neurostimulator placement can optimize postoperative seizure outcomes when guided by fast ripple networks.

In medication-resistant epilepsy, the goal of epilepsy surgery is to make a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure-onset zone and incorporation of neuroimaging findings from MRI, PET, single-photon emission CT and magnetoencephalography modalities. Resecting cortical tissue generating high-frequency oscillations has been investigated as a more efficacious alternative to targeting the seizure-onset zone. In this study, we used a support vector machine (SVM), with four distinct fast ripple (FR: 350-600 Hz on oscillations, 200-600 Hz on spikes) metrics as factors. These metrics included the FR resection ratio, a spatial FR network measure and two temporal FR network measures. The SVM was trained by the value of these four factors with respect to the actual resection boundaries and actual seizure-free labels of 18 patients with medically refractory focal epilepsy. Leave-one-out cross-validation of the trained SVM in this training set had an accuracy of 0.78. We next used a simulated iterative virtual resection targeting the FR sites that were of highest rate and showed most temporal autonomy. The trained SVM utilized the four virtual FR metrics to predict virtual seizure freedom. In all but one of the nine patients who were seizure free after surgery, we found that the virtual resections sufficient for virtual seizure freedom were larger in volume (P < 0.05). In nine patients who were not seizure free, a larger virtual resection made five virtually seizure free. We also examined 10 medically refractory focal epilepsy patients implanted with the responsive neurostimulator system and virtually targeted the responsive neurostimulator system stimulation contacts proximal to sites generating FR at highest rates to determine if the simulated value of the stimulated seizure-onset zone and stimulated FR metrics would trend towards those patients with a better seizure outcome. Our results suggest the following: (i) FR measures can accurately predict whether a resection, defined by the standard of care, will result in seizure freedom; (ii) utilizing FR alone for planning an efficacious surgery can be associated with larger resections; (iii) when FR metrics predict the standard-of-care resection will fail, amending the boundaries of the planned resection with certain FR-generating sites may improve outcome and (iv) more work is required to determine whether targeting responsive neurostimulator system stimulation contact proximal to FR generating sites will improve seizure outcome.

Simulated resections and RNS placement can optimize post-operative seizure outcomes when guided by fast ripple networks.

In medication-resistant epilepsy, the goal of epilepsy surgery is to make a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure onset zone (SOZ) and incorporation of neuroimaging findings from MRI, PET, SPECT, and MEG modalities. Resecting cortical tissue generating high-frequency oscillations (HFOs) has been investigated as a more efficacious alternative to targeting the SOZ. In this study, we used a support vector machine (SVM), with four distinct fast ripple (FR: 350-600 Hz on oscillations, 200-600 Hz on spikes) metrics as factors. These metrics included the FR resection ratio (RR), a spatial FR network measure, and two temporal FR network measures. The SVM was trained by the value of these four factors with respect to the actual resection boundaries and actual seizure free labels of 18 patients with medically refractory focal epilepsy. Leave one out cross-validation of the trained SVM in this training set had an accuracy of 0.78. We next used a simulated iterative virtual resection targeting the FR sites that were highest rate and showed most temporal autonomy. The trained SVM utilized the four virtual FR metrics to predict virtual seizure freedom. In all but one of the nine patients seizure free after surgery, we found that the virtual resections sufficient for virtual seizure freedom were larger in volume (p<0.05). In nine patients who were not seizure free, a larger virtual resection made five virtually seizure free. We also examined 10 medically refractory focal epilepsy patients implanted with the responsive neurostimulator system (RNS) and virtually targeted the RNS stimulation contacts proximal to sites generating FR at highest rates to determine if the simulated value of the stimulated SOZ and stimulated FR metrics would trend toward those patients with a better seizure outcome. Our results suggest: 1) FR measures can accurately predict whether a resection, defined by the standard of care, will result in seizure freedom; 2) utilizing FR alone for planning an efficacious surgery can be associated with larger resections; 3) when FR metrics predict the standard of care resection will fail, amending the boundaries of the planned resection with certain FR generating sites may improve outcome; and 4) more work is required to determine if targeting RNS stimulation contact proximal to FR generating sites will improve seizure outcome.

Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications.

Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits - each requiring fine, tailored modulation - that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition.

Real-time emotion detection by quantitative facial motion analysis.

BACKGROUND: Research into mood and emotion has often depended on slow and subjective self-report, highlighting a need for rapid, accurate, and objective assessment tools. METHODS: To address this gap, we developed a method using digital image speckle correlation (DISC), which tracks subtle changes in facial expressions invisible to the naked eye, to assess emotions in real-time. We presented ten participants with visual stimuli triggering neutral, happy, and sad emotions and quantified their associated facial responses via detailed DISC analysis. RESULTS: We identified key alterations in facial expression (facial maps) that reliably signal changes in mood state across all individuals based on these data. Furthermore, principal component analysis of these facial maps identified regions associated with happy and sad emotions. Compared with commercial deep learning solutions that use individual images to detect facial expressions and classify emotions, such as Amazon Rekognition, our DISC-based classifiers utilize frame-to-frame changes. Our data show that DISC-based classifiers deliver substantially better predictions, and they are inherently free of racial or gender bias. LIMITATIONS: Our sample size was limited, and participants were aware their faces were recorded on video. Despite this, our results remained consistent across individuals. CONCLUSIONS: We demonstrate that DISC-based facial analysis can be used to reliably identify an individual's emotion and may provide a robust and economic modality for real-time, noninvasive clinical monitoring in the future.

Brain Circuitry of Consciousness: A Review of Current Models and a Novel Synergistic Model With Clinical Application.

How consciousness arises in the brain has important implications for clinical decision-making. We summarize recent findings in consciousness studies to provide a toolkit for clinicians to assess deficits in consciousness and predict outcomes after brain injury. Commonly encountered disorders of consciousness are highlighted, followed by the clinical scales currently used to diagnose them. We review recent evidence describing the roles of the thalamocortical system and brainstem arousal nuclei in supporting awareness and arousal and discuss the utility of various neuroimaging studies in evaluating disorders of consciousness. We explore recent theoretical progress in mechanistic models of consciousness, focusing on 2 major models, the global neuronal workspace and integrated information theory, and review areas of controversy. Finally, we consider the potential implications of recent research for the day-to-day decision-making of clinical neurosurgeons and propose a simple "three-strikes" model to infer the integrity of the thalamocortical system, which can guide prognosticating return to consciousness.

In-Hospital Amantadine Does Not Improve Outcomes After Severe Traumatic Brain Injury: An 11-Year Propensity-Matched Retrospective Analysis.

BACKGROUND: Disruption of dopamine neurotransmission is associated with functional impairment after severe traumatic brain injury (sTBI). This has prompted the study of dopamine agonists, such as amantadine, to assist recovery of consciousness. Randomized trials have mostly addressed the posthospital setting, with inconsistent findings. Therefore, we evaluated the efficacy of early amantadine administration on recovery of consciousness after sTBI. METHODS: We searched the medical records of all patients with sTBI admitted to our hospital between 2010 and 2021 who survived 10 days postinjury. We identified all patients receiving amantadine and compared them with all patients not receiving amantadine and a propensity score-matched nonamantadine group. Primary outcome measures included discharge Glasgow Coma Scale, Glasgow Outcome Scale-Extended score, length of stay, mortality, recovery of command-following (CF), and days to CF. RESULTS: In our study population, 60 patients received amantadine and 344 did not. Compared with the propensity score-matched nonamantadine group, the amantadine group had no difference in mortality (86.67% vs. 88.33%, P = 0.783), rates of CF (73.33% vs. 76.67%, P = 0.673), or percentage of patients with severe (3-8) discharge Glasgow Coma Scale scores (11.11% vs. 12.28%, P = 0.434). In addition, the amantadine group was less likely to have a favorable recovery (discharge Glasgow Outcome Scale-Extended score 5-8) (14.53% vs. 16.67%, P < 0.001), had a longer length of stay (40.5 vs. 21.0 days, P < 0.001), and had a longer time to CF (11.5 vs. 6.0 days, P = 0.011). No difference in adverse events existed between groups. CONCLUSIONS: Our findings do not support the early administration of amantadine for sTBI. Larger inpatient randomized trials are necessary to further investigate amantadine treatment for sTBI.

Predicting traumatic brain injury outcomes using a posterior dominant rhythm.

OBJECTIVE: Predicting severe traumatic brain injury (sTBI) outcomes is challenging, and existing models have limited applicability to individual patients. This study aimed to identify metrics that could predict recovery following sTBI. The researchers strived to demonstrate that a posterior dominant rhythm on electroencephalography is strongly associated with positive outcomes and to develop a novel machine learning-based model that accurately forecasts the return of consciousness. METHODS: In this retrospective study, the authors assessed all intubated adults admitted with sTBI (Glasgow Coma Scale [GCS] score ≤ 8) from 2010 to 2021, who underwent EEG recording < 30 days from sTBI (n = 195). Seventy-three clinical, radiographic, and EEG variables were collected. Based on the presence of a PDR within 30 days of injury, two cohorts were created-those with a PDR (PDR[+] cohort, n = 51) and those without (PDR[-] cohort, n = 144)-to assess differences in presentation and four outcomes: in-hospital survival, recovery of command following, Glasgow Outcome Scale-Extended (GOS-E) score at discharge, and GOS-E score at 6 months post discharge. AutoScore, a machine learning-based clinical score generator that selects and assigns weights to important predictive variables, was used to create a prognostic model that predicts in-hospital survival and recovery of command following. Lastly, the MRC-CRASH and IMPACT traumatic brain injury predictive models were used to compare expected patient outcomes with true outcomes. RESULTS: At presentation, the PDR(-) cohort had a lower mean GCS motor subscore (1.97 vs 2.45, p = 0.048). Despite no difference in predicted outcomes (via MRC-CRASH and IMPACT), the PDR(+) cohort had superior rates of in-hospital survival (84.3% vs 63.9%, p = 0.007), recovery of command following (76.5% vs 53.5%, p = 0.004), and mean discharge GOS-E score (3.00 vs 2.39, p = 0.006). There was no difference in the 6-month GOS-E score. AutoScore was then used to identify the 7 following variables that were highly predictive of in-hospital survival and recovery of command: age, body mass index, systolic blood pressure, pupil reactivity, blood glucose, and hemoglobin (all at presentation), and a PDR on EEG. This model had excellent discrimination for predicting in-hospital survival (area under the curve [AUC] 0.815) and recovery of command following (AUC 0.700). CONCLUSIONS: A PDR on EEG in sTBI patients predicts favorable outcomes. The authors' prognostic model has strong accuracy in predicting these outcomes, and performed better than previously reported models. The authors' model can be valuable in clinical decision-making as well as counseling families following these types of injuries.

Self-assembled magnetic bead biosensor for measuring bacterial growth and antimicrobial susceptibility testing.

Bacterial antibiotic resistance is one of the major concerns of modern healthcare worldwide, and the development of rapid, growth-based, antimicrobial susceptibility tests is key for addressing it. The cover image shows a self-assembled asynchronous magnetic bead rotation (AMBR) biosensor developed for rapid detection of bacterial growth. Using the biosensors, the minimum inhibitory concentration of a clinical E. coli isolate can be measured within two hours, where currently tests take 6-24 hours. A 16-well prototype is also constructed for simple and robust observation of the self-assembled AMBR biosensors.

Cortical recordings reveal hidden early signs of recovery following traumatic brain injury: A case report.

Prognosticating recovery of consciousness after severe traumatic brain injury (TBI) is a difficult task. Understanding the mechanism of recovery of consciousness in these patients will undoubtedly help clarify this issue. Recent research has underscored the importance of electrophysiological data in characterizing the state of the brain during this period of unconsciousness. Here, we investigated cortical electrophysiological recordings from a single TBI patient and discovered that high-frequency activity associated with the return of consciousness reappeared in a spatiotemporal fashion. We observed a shift toward higher frequencies first in the anterior cingulate cortex, and then later in the dorsolateral prefrontal cortex. This finding suggests that recovery may originate in more internal cortices and progress to superficial ones. Although this observation occurred in a single patient, it points to a potential mechanism for recovery of normal cortical activity in the return of consciousness following TBI.

Injury to thalamocortical projections following traumatic brain injury results in attractor dynamics for cortical networks.

Major theories of consciousness predict that complex electroencephalographic (EEG) activity is required for consciousness, yet it is not clear how such activity arises in the corticothalamic system. The thalamus is well-known to control cortical excitability via interlaminar projections, but whether thalamic input is needed for complexity is not known. We hypothesized that the thalamus facilitates complex activity by adjusting synaptic connectivity, thereby increasing the availability of different configurations of cortical neurons (cortical "states"), as well as the probability of state transitions. To test this hypothesis, we characterized EEG activity from prefrontal cortex (PFC) in traumatic brain injury (TBI) patients with and without injuries to thalamocortical projections, measured with diffusion tensor imaging (DTI). We found that injury to thalamic projections (especially from the mediodorsal thalamus) was strongly associated with unconsciousness and delta-band EEG activity. Using advanced signal processing techniques, we found that lack of thalamic input led to 1.) attractor dynamics for cortical networks with a tendency to visit the same states, 2.) a reduced repertoire of possible states, and 3.) high predictability of transitions between states. These results imply that complex PFC activity associated with consciousness depends on thalamic input. Our model implies that restoration of cortical connectivity is a critical function of the thalamus after brain injury. We draw a critical connection between thalamic input and complex cortical activity associated with consciousness.

Low-molecular-weight heparin compared with unfractionated heparin in critically ill COVID-19 patients.

BACKGROUND: Thrombosis in COVID-19 worsens mortality. In our study, we sought to investigate how the dose and type of anticoagulation (AC) can influence patient outcomes. METHODS: This is a single-center retrospective analysis of critically ill intubated patients with COVID-19, comparing low-molecular-weight heparin (LMWH) and unfractionated heparin (UFH) at therapeutic and prophylactic doses. Of 218 patients, 135 received LMWH (70 prophylactic, 65 therapeutic) and 83 UFH (11 prophylactic, 72 therapeutic). The primary outcome was mortality. Secondary outcomes were thromboembolic complications confirmed on imaging and major bleeding complications. Cox proportional-hazards regression models were used to determine whether the type and dose of AC were independent predictors of survival. We performed Kaplan-Meier survival analysis to compare the cumulative survivals. RESULTS: Overall, therapeutic AC, with either LMWH (65% vs 79%, P = .09) or UFH (32% vs 46%, P = .73), conveyed no survival benefit over prophylactic AC. UFH was associated with a higher mortality rate than LMWH (66% vs 28%, P = .001), which was also evident in the multivariable analysis (LMWH vs UFH mortality, hazard ratio: 0.47, P = .001) and in the Kaplan-Meier survival analysis. Thrombotic and bleeding complications did not depend on the AC type (prophylactic LMWH vs UFH: thrombosis P = .49, bleeding P = .075; therapeutic LMWH vs UFH: thrombosis P = .5, bleeding P = .17). When comparing prophylactic with therapeutic AC, the rate of both thrombotic and bleeding complications was higher with the use of LMWH compared with UFH. In addition, transfusion requirements were significantly higher with both therapeutic LMWH and UFH. CONCLUSIONS: Among intubated critically ill COVID-19 intensive care unit patients, therapeutic AC, with either LMWH or UFH, conveyed no survival benefit over prophylactic AC. AC with LMWH was associated with higher cumulative survival compared with AC with UFH.

Acute Gastrointestinal Injury and Feeding Intolerance as Prognostic Factors in Critically Ill COVID-19 Patients.

BACKGROUND: Although acute gastrointestinal injury (AGI) and feeding intolerance (FI) are known independent determinants of worse outcomes and high mortality in intensive care unit (ICU) patients, the incidence of AGI and FI in critically ill COVID-19 patients and their prognostic importance have not been thoroughly studied. METHODS: We reviewed 218 intubated patients at Stony Brook University Hospital and stratified them into three groups based on AGI severity, according to data collected in the first 10 days of ICU course. We used chi-square test to compare categorical variables such as age and sex and two-sample t-test or Mann-Whitney U-tests for continuous variables, including important laboratory values. Cox proportional hazards regression models were utilized to determine whether AGI score was an independent predictor of survival, and multivariable analysis was performed to compare risk factors that were deemed significant in the univariable analysis. We performed Kaplan-Meier survival analysis based on the AGI score and the presence of FI. RESULTS: The overall incidence of AGI was 95% (45% AGI I/II, 50% AGI III/IV), and FI incidence was 63%. Patients with AGI III/IV were more likely to have prolonged mechanical ventilation (22 days vs 16 days, P-value <0.002) and higher mortality rate (58% vs 28%, P-value <0.001) compared to patients with AGI 0/I/II. This was confirmed with multivariable analysis which showed that AGI score III/IV was an independent predictor of higher mortality (AGI III/IV vs AGI 0/I/II hazard ratio (HR), 2.68; 95% confidence interval (CI), 1.69-4.25; P-value <0.0001). Kaplan-Meier survival analysis showed that both AGI III/IV and FI (P-value <0.001) were associated with worse outcomes. Patients with AGI III/IV had higher daily and mean D-dimer and CRP levels compared to AGI 0/I/II (P-value <0.0001). CONCLUSIONS: The prevalence of AGI and FI among critically ill COVID-19 patients was high. AGI grades III/IV were associated with higher risk for prolonged mechanical ventilation and mortality compared to AGI 0/I/II, while it also correlated with higher D-dimer and C-reactive protein (CRP) levels. FI was independently associated with higher mortality. The development of high-grade AGI and FI during the first days of ICU stay can serve as prognostic tools to predict outcomes in critically ill COVID-19 patients.

Thalamo-Prefrontal Connectivity Correlates With Early Command-Following After Severe Traumatic Brain Injury.

Recovery of consciousness after traumatic brain injury (TBI) is heterogeneous and difficult to predict. Structures such as the thalamus and prefrontal cortex are thought to be important in facilitating consciousness. We sought to investigate whether the integrity of thalamo-prefrontal circuits, assessed via diffusion tensor imaging (DTI), was associated with the return of goal-directed behavior after severe TBI. We classified a cohort of severe TBI patients (N = 25, 20 males) into Early and Late/Never outcome groups based on their ability to follow commands within 30 days post-injury. We assessed connectivity between whole thalamus, and mediodorsal thalamus (MD), to prefrontal cortex (PFC) subregions including dorsolateral PFC (dlPFC), medial PFC (mPFC), anterior cingulate (ACC), and orbitofrontal (OFC) cortices. We found that the integrity of thalamic projections to PFC subregions (L OFC, L and R ACC, and R mPFC) was significantly associated with Early command-following. This association persisted when the analysis was restricted to prefrontal-mediodorsal (MD) thalamus connectivity. In contrast, dlPFC connectivity to thalamus was not significantly associated with command-following. Using the integrity of thalamo-prefrontal connections, we created a linear regression model that demonstrated 72% accuracy in predicting command-following after a leave-one-out analysis. Together, these data support a role for thalamo-prefrontal connectivity in the return of goal-directed behavior following TBI.

Aggressive Anticoagulation May Decrease Mortality in Obese Critically Ill COVID-19 Patients.

BACKGROUND: Obesity is a widely accepted risk factor for the development of severe COVID-19. We sought to determine the survival benefit of early initiation of aggressive anticoagulation in obese critically ill COVID-19 patients. METHODS: We retrospectively reviewed 237 intubated patients at a single academic accredited bariatric center and stratified them based on their BMI into 2 groups, obese (BMI > 30) and non-obese (BMI ≤ 30). We used chi-square tests to compare categorical variables such as age and sex, and two-sample t-tests or Mann Whitney U-tests for continuous variables, including important laboratory values. Cox proportional-hazards regression models were utilized to determine whether obesity was an independent predictor of survival and multivariable analysis was performed to compare risk factors that were deemed significant in the univariable analysis. Survival with respect to BMI and its association with level of anticoagulation in the obese cohort was evaluated using Kaplan-Meier models. RESULTS: The overall mortality in the obese and non-obese groups was similar at 47% and 44%, respectively (p = 0.65). Further analysis based on the level of AC showed that obese patients placed on early aggressive AC protocol had improved survival compared to obese patients who did not receive protocol based aggressive AC (ON-aggressive AC protocol 26% versus OFF-aggressive AC protocol 61%, p = 0.0004). CONCLUSIONS: The implementation of early aggressive anticoagulation may balance the negative effects of obesity on the overall mortality in critically ill COVID-19 patients.

Cardiac arrest after severe traumatic brain injury can be survivable with good outcomes.

BACKGROUND: Resuscitation for traumatic cardiac arrest (TCA) in patients with severe traumatic brain injury (sTBI) has historically been considered futile. There is little information on the characteristics and outcomes of these patients to guide intervention and prognosis. The purpose of the current study is to report the clinical characteristics, survival, and long-term neurological outcomes in patients who experienced TCA after sTBI and analyze the factors contributing to survival. METHODS: A retrospective review identified 42 patients with TCA from a total of 402 patients with sTBI (Glasgow Coma Scale (GCS) score ≤8) who were admitted to Stony Brook University Hospital, a level I trauma center, from January 2011 to December 2018. Patient demographics, clinical characteristics, survival, and neurological functioning during hospitalization and at follow-up visits were collected. RESULTS: Of the 42 patients, the average age was 45 years and 21.4% were female. Eight patients survived the injury (19.0%) to discharge and seven survived with good neurological function. Admission GCS score and bilateral pupil reactivity were found to be significant indicators of survival. The mean GCS score was 5.3 in survivors and 3.2 in non-survivors (p=0.020). Age, Injury Severity Score, or cardiac rhythm was not associated with survival. Frequent neuroimaging findings included subarachnoid hemorrhage, subdural hematoma, and diffuse axonal injury. DISCUSSION: TCA after sTBI is survivable and seven out of eight patients in our study recovered with good neurological function. GCS score and pupil reactivity are the best indicators of survival. Our results suggest that due to the possibility of recovery, resuscitation and neurosurgical care should not be withheld from this patient population. LEVEL OF EVIDENCE: Level IV, therapeutic/care management.