NBS-Predict: A prediction-based extension of the network-based statistic.

Graph models of the brain hold great promise as a framework to study functional and structural brain connectivity across scales and species. The network-based statistic (NBS) is a well-known tool for performing statistical inference on brain graphs, which controls the family-wise error rate in a mass univariate analysis by combining the cluster-based permutation technique and the graph-theoretical concept of connected components. As the NBS is based on group-level inference statistics, it does not inherently enable informed decisions at the level of individuals, which is, however, necessary for the realm of precision medicine. Here we introduce NBS-Predict, a new approach that combines the powerful features of machine learning (ML) and the NBS in a user-friendly graphical user interface (GUI). By combining ML models with connected components in a cross-validation (CV) structure, the new methodology provides a fast and convenient tool to identify generalizable neuroimaging-based biomarkers. The purpose of this paper is to (i) introduce NBS-Predict and evaluate its performance using two sets of simulated data with known ground truths, (ii) demonstrate the application of NBS-Predict in a real case-control study, including resting-state functional magnetic resonance imaging (rs-fMRI) data acquired from patients with schizophrenia, (iii) evaluate NBS-Predict using rs-fMRI data from the Human Connectome Project 1200 subjects release. We found that: (i) NBS-Predict achieved good statistical power on two sets of simulated data; (ii) NBS-Predict classified schizophrenia with an accuracy of 90% using subjects' functional connectivity matrices and identified a subnetwork with reduced connections in the group with schizophrenia, mainly comprising brain regions localized in frontotemporal, visual, and motor areas, as well as in the subcortex; (iii) NBS-Predict also predicted general intelligence scores from resting-state fMRI connectivity matrices with a prediction score of r = 0.2 and identified a large-scale subnetwork associated with general intelligence. Overall results showed that NBS-Predict performed comparable to or better than pre-existing feature selection algorithms (lasso, elastic net, top 5%, p-value thresholding) and connectome-based predictive modeling (CPM) in terms of identifying relevant features and prediction accuracy.

Electrocerebral Signature of Cardiac Death.

BACKGROUND: Electroencephalography (EEG) findings following cardiovascular collapse in death are uncertain. We aimed to characterize EEG changes immediately preceding and following cardiac death. METHODS: We retrospectively analyzed EEGs of patients who died from cardiac arrest while undergoing standard EEG monitoring in an intensive care unit. Patients with brain death preceding cardiac death were excluded. Three events during fatal cardiovascular failure were investigated: (1) last recorded QRS complex on electrocardiogram (QRS0), (2) cessation of cerebral blood flow (CBF0) estimated as the time that blood pressure and heart rate dropped below set thresholds, and (3) electrocerebral silence on EEG (EEG0). We evaluated EEG spectral power, coherence, and permutation entropy at these time points. RESULTS: Among 19 patients who died while undergoing EEG monitoring, seven (37%) had a comfort-measures-only status and 18 (95%) had a do-not-resuscitate status in place at the time of death. EEG0 occurred at the time of QRS0 in five patients and after QRS0 in two patients (cohort median - 2.0, interquartile range - 8.0 to 0.0), whereas EEG0 was seen at the time of CBF0 in six patients and following CBF0 in 11 patients (cohort median 2.0 min, interquartile range - 1.5 to 6.0). After CBF0, full-spectrum log power (p < 0.001) and coherence (p < 0.001) decreased on EEG, whereas delta (p = 0.007) and theta (p < 0.001) permutation entropy increased. CONCLUSIONS: Rarely may patients have transient electrocerebral activity following the last recorded QRS (less than 5 min) and estimated cessation of cerebral blood flow. These results may have implications for discussions around cardiopulmonary resuscitation and organ donation.

Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury.

BACKGROUND: Brain activation in response to spoken motor commands can be detected by electroencephalography (EEG) in clinically unresponsive patients. The prevalence and prognostic importance of a dissociation between commanded motor behavior and brain activation in the first few days after brain injury are not well understood. METHODS: We studied a prospective, consecutive series of patients in a single intensive care unit who had acute brain injury from a variety of causes and who were unresponsive to spoken commands, including some patients with the ability to localize painful stimuli or to fixate on or track visual stimuli. Machine learning was applied to EEG recordings to detect brain activation in response to commands that patients move their hands. The functional outcome at 12 months was determined with the Glasgow Outcome Scale-Extended (GOS-E; levels range from 1 to 8, with higher levels indicating better outcomes). RESULTS: A total of 16 of 104 unresponsive patients (15%) had brain activation detected by EEG at a median of 4 days after injury. The condition in 8 of these 16 patients (50%) and in 23 of 88 patients (26%) without brain activation improved such that they were able to follow commands before discharge. At 12 months, 7 of 16 patients (44%) with brain activation and 12 of 84 patients (14%) without brain activation had a GOS-E level of 4 or higher, denoting the ability to function independently for 8 hours (odds ratio, 4.6; 95% confidence interval, 1.2 to 17.1). CONCLUSIONS: A dissociation between the absence of behavioral responses to motor commands and the evidence of brain activation in response to these commands in EEG recordings was found in 15% of patients in a consecutive series of patients with acute brain injury. (Supported by the Dana Foundation and the James S. McDonnell Foundation.).

Deep structural brain lesions associated with consciousness impairment early after hemorrhagic stroke.

The purpose of this study was to determine the significance of deep structural lesions for impairment of consciousness following hemorrhagic stroke and recovery at ICU discharge. Our study focused on deep lesions that previously were implicated in studies of disorders of consciousness. We analyzed MRI measures obtained within the first week of the bleed and command following throughout the ICU stay. A machine learning approach was applied to identify MRI findings that best predicted the level consciousness. From 158 intracerebral hemorrhage patients that underwent MRI, one third was unconscious at the time of MRI and half of these patients recovered consciousness by ICU discharge. Deep structural lesions predicted both, impairment and recovery of consciousness, together with established measures of mass effect. Lesions in the midbrain peduncle and pontine tegmentum alongside the caudate nucleus were implicated as critical structures. Unconscious patients predicted to recover consciousness by ICU discharge had better long-term functional outcomes than those predicted to remain unconscious.