Cortical Spreading Depolarizations and Clinically Measured Scalp EEG Activity After Aneurysmal Subarachnoid Hemorrhage and Traumatic Brain Injury

Traumatic Brain Injury and Traumatic Spinal Cord Injury

OBJECTIVE

This article reviews the mechanisms of primary traumatic injury to the brain and spinal cord, with an emphasis on grading severity, identifying surgical indications, anticipating complications, and managing secondary injury.

LATEST DEVELOPMENTS

Serum biomarkers have emerged for clinical decision making and prognosis after traumatic injury. Cortical spreading depolarization has been identified as a potentially modifiable mechanism of secondary injury after traumatic brain injury. Innovative methods to detect covert consciousness may inform prognosis and enrich future studies of coma recovery. The time-sensitive nature of spinal decompression is being elucidated.

ESSENTIAL POINTS

Proven management strategies for patients with severe neurotrauma in the intensive care unit include surgical decompression when appropriate, the optimization of perfusion, and the anticipation and treatment of complications. Despite validated models, predicting outcomes after traumatic brain injury remains challenging, requiring prognostic humility and a model of shared decision making with surrogate decision makers to establish care goals. Penetrating injuries, especially gunshot wounds, are often devastating and require public health and policy approaches that target prevention.

Similarities in the Electrographic Patterns of Delayed Cerebral Infarction and Brain Death After Aneurysmal and Traumatic Subarachnoid Hemorrhage

While subarachnoid hemorrhage is the second most common hemorrhagic stroke in epidemiologic studies, the recent DISCHARGE-1 trial has shown that in reality, three-quarters of focal brain damage after subarachnoid hemorrhage is ischemic. Two-fifths of these ischemic infarctions occur early and three-fifths are delayed. The vast majority are cortical infarcts whose pathomorphology corresponds to anemic infarcts. Therefore, we propose in this review that subarachnoid hemorrhage as an ischemic-hemorrhagic stroke is rather a third, separate entity in addition to purely ischemic or hemorrhagic strokes. Cumulative focal brain damage, determined by neuroimaging after the first 2 weeks, is the strongest known predictor of patient outcome half a year after the initial hemorrhage. Because of the unique ability to implant neuromonitoring probes at the brain surface before stroke onset and to perform longitudinal MRI scans before and after stroke, delayed cerebral ischemia is currently the stroke variant in humans whose pathophysiological details are by far the best characterized. Optoelectrodes located directly over newly developing delayed infarcts have shown that, as mechanistic correlates of infarct development, spreading depolarizations trigger (1) spreading ischemia, (2) severe hypoxia, (3) persistent activity depression, and (4) transition from clustered spreading depolarizations to a negative ultraslow potential. Furthermore, traumatic brain injury and subarachnoid hemorrhage are the second and third most common etiologies of brain death during continued systemic circulation. Here, we use examples to illustrate that although the pathophysiological cascades associated with brain death are global, they closely resemble the local cascades associated with the development of delayed cerebral infarcts.

Noninvasive and reliable automated detection of spreading depolarization in severe traumatic brain injury using scalp EEG

BACKGROUND

Spreading depolarizations (SDs) are a biomarker and a potentially treatable mechanism of worsening brain injury after traumatic brain injury (TBI). Noninvasive detection of SDs could transform critical care for brain injury patients but has remained elusive. Current methods to detect SDs are based on invasive intracranial recordings with limited spatial coverage. In this study, we establish the feasibility of automated SD detection through noninvasive scalp electroencephalography (EEG) for patients with severe TBI.

METHODS

Building on our recent WAVEFRONT algorithm, we designed an automated SD detection method. This algorithm, with learnable parameters and improved velocity estimation, extracts and tracks propagating power depressions using low-density EEG. The dataset for testing our algorithm contains 700 total SDs in 12 severe TBI patients who underwent decompressive hemicraniectomy (DHC), labeled using ground-truth intracranial EEG recordings. We utilize simultaneously recorded, continuous, low-density (19 electrodes) scalp EEG signals, to quantify the detection accuracy of WAVEFRONT in terms of true positive rate (TPR), false positive rate (FPR), as well as the accuracy of estimating SD frequency.

RESULTS

WAVEFRONT achieves the best average validation accuracy using Delta band EEG: 74% TPR with less than 1.5% FPR. Further, preliminary evidence suggests WAVEFRONT can estimate how frequently SDs may occur.

CONCLUSIONS

We establish the feasibility, and quantify the performance, of noninvasive SD detection after severe TBI using an automated algorithm. The algorithm, WAVEFRONT, can also potentially be used for diagnosis, monitoring, and tailoring treatments for worsening brain injury. Extension of these results to patients with intact skulls requires further study.

A Spatial-Temporal Graph Attention Network for Automated Detection and Width Estimation of Cortical Spreading Depression Using Scalp EEG

We present an end-to-end Spatial-Temporal Graph Attention Network (STGAT) for non-invasive detection and width estimation of Cortical Spreading Depressions (CSDs) on scalp electroencephalography (EEG). Our algorithm, that we refer to as CSD Spatial-temporal graph attention network or CSD-STGAT, is trained and tested on simulated CSDs with varying width and speed ranges. Using high-density EEG, CSD-STGAT achieves less than 10.96% normalized width estimation error for narrow CSDs, with an average normalized error of 6.35%±3.08% across all widths, enabling non-invasive and automated estimation of the width of CSDs for the first time. In addition, CSD-STGAT learns the temporal and spatial features of CSDs simultaneously, which improves the "spatio-temporal tracking accuracy" (i.e., the defined detection performance metric at each electrode) of the narrow CSDs by up to 14%, compared to the state-of-the-art CSD-SpArC algorithm, with only one-tenth of the network size. CSD-STGAT achieves the best spatio-temporal tracking accuracy of 86.27%±0.53% for wide CSDs using high-density EEG, which is comparable to the performance of CSD-SpArC with less than 0.38% performance reduction. We further stitch the detections across all electrodes and over time to evaluate the "temporal accuracy". Our algorithm achieves less than 0.7% false positive rate in the simulated dataset with inter-CSD intervals ranging from 5 to 60 minutes. The lightweight architecture of CSD-STGAT paves the way towards real-time detection and parameter estimation of these waves in the brain, with significant clinical impact.