An Algorithm for Automated, Noninvasive Detection of Cortical Spreading Depolarizations Based on EEG Simulations

The role of high-density EEG in diagnosis and prognosis of neurological diseases: A systematic review

OBJECTIVE

The use of High-Density Electroencephalography (HD-EEG) increased in neurological disorders, due to analysis of brain connectivity. This method is able to create a detailed brain mapping. The aim is to investigate studies that employed HD-EEG in neurological and neurodegenerative filed.

METHODS

This systematic review was conducted and reported in accordance with the PRISMA. A research terms was conducted for: (1) dementia, (2) Multiple Sclerosis (MS), (3) Parkinson Disease (PD), (4) stroke, (5) epilepsy.

RESULTS

The study included a total of 89 articles: 22 dementia; 33 epilepsy; 5 MS; 24 PD; 5 S. Articles were discussed for each neurological disorder and for different types of EEG analysis: analysis of event-related potentials, specific EEG features at resting state, spectral and connectivity analysis, time-frequency analysis and EEG recordings combined with other types of intervention.

DISCUSSION

HD-EEG recordings provide evidence about the evaluation of early markers of the disease onset, mapping of cortical activity distribution of neurological disorders.

SIGNIFICANCE

HD-EEG demonstrated it effectiveness in detection of biomarkers for the diagnosis and prognosis. In dementia contributed to misdiagnosis between different subtype and identifying markers of cognitive decline, investigating motor and cognitive networks dynamics in stroke, PD and MS, and to detect task-specific network reorganization.

Electroconvulsive therapy generates a postictal wave of spreading depolarization in mice and humans

Electroconvulsive therapy (ECT) is a fast-acting, highly effective, and safe treatment for medication-resistant depression. Historically, the clinical benefits of ECT have been attributed to generating a controlled seizure; however, the underlying neurobiology is understudied and unresolved. Using optical neuroimaging of neural activity and hemodynamics in a mouse model of ECT, we demonstrated that a second brain event follows seizure: cortical spreading depolarization (CSD). We found that ECT pulse parameters and electrode configuration directly shaped the wave dynamics of seizure and subsequent CSD. To translate these findings to human patients, we used non-invasive diffuse optical monitoring of cerebral blood flow and oxygenation during routine ECT treatments. We observed that human brains reliably generate hyperemic waves after ECT seizure which are highly consistent with CSD. These results challenge a long-held assumption that seizure is the primary outcome of ECT and point to new opportunities for optimizing ECT stimulation parameters and treatment outcomes.

Proof-of-Concept Validation of Noninvasive Detection of Cortical Spreading Depolarization with High Resolution Direct Current-Electroencephalography

Background/Objective

Cortical spreading depolarization (SD) is increasingly recognized as a major contributor to secondary brain injury. Noninvasive SD monitoring would enable the institution of SD-based therapeutics. Our primary objective is to establish proof-of-concept validation that scalp DC-potentials can provide noninvasive SD detection by comparing scalp direct-current (DC)-shifts from a high-density electrode array to SDs detected by gold-standard electrocorticography (ECoG). Our secondary objective is to assess usability and artifact tolerance.

Methods

An 83×58 mm thermoplastic elastomer array with 29 6-mm diameter Ag/AgCl 1-cm spaced electrodes, the CerebroPatch™ Proof-of-Concept Prototype, was adhesively placed on the forehead with an intervening electrode gel interface to record DC-electroencephalography in normal volunteers and severe acute brain injury patients in the neuro-intensive care unit some with and some without invasive ECoG electrodes. The scalp and ECoG voltages were collected by a Moberg® Advanced ICU Amplifier. Artifacts were visually identified and usability issues were recorded. SD was scored on ECoG based on DC-shifts with associated high-frequency suppression and propagation. A six-parameter Gaussian plus quadratic baseline model was used to estimate ECoG and scalp electrode time-courses and scalp-voltage heat-map movies. The similarity of the noninvasive scalp and invasive ECoG DC-shift time-courses was compared via the Gaussian fit parameters and confirmed if the Coefficient-of-Determination was >0.80.

Results

Usability and artifact issues obscured most scalp Prototype device data of the 140 ECoG-coded SDs during 11 days in one sub-arachnoid hemorrhage patient. Twenty-six of these DC-shifts were in readable, artifact-free portions of scalp recordings and 24 of these had a >0.80 Coefficient-of-Determination (0.98[0.02], median[IQR]) between invasive ECoG and noninvasive Prototype device DC-shifts. Reconstructed heat-map movies of the scalp DC-potentials showed a 5-cm extent, -460 μV peak region that persisted for ~70 sec. These data suggest that these scalp DC-shifts (peak -457±69 μV [mean±StD], full-width-half maximum 70.9±5.92 sec, area 18.7±2.76 cm2) depicted in the heat-map movies represent noninvasively detected SDs.

Conclusions

These results using 26 SDs as the observational units suggest that noninvasive SD detection is possible using scalp DC-potential signals with a high spatial resolution EEG array. Although the high artifact burden data and low usability records were limiting, negative results, they serve as an important entrepreneurial recipe for a future, re-designed device that would reduce artifacts and improve usability for DC-EEG SD detection needed to enable multi-modal monitoring for secondary brain injury.

Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach

A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocorticography recorded at high sampling frequency. Recent pilot studies suggest a possible utility of scalp electrodes generated electroencephalogram (EEG) for non-invasive SD detection. However, noise and attenuation of EEG signals makes this detection task extremely challenging. Previous methods focus on detecting temporal power change of EEG over a fixed high-density map of scalp electrodes, which is not always clinically feasible. Having a specialized spectrogram as an input to the automatic SD detection model, this study is the first to transform SD identification problem from a detection task on a 1-D time-series wave to a task on a sequential 2-D rendered imaging. This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning. Our proposed model has an ultra-fast processing speed (<0.3 sec). Compared to the conventional methods (2 hours), this is a huge advancement towards early SD detection and to facilitate instant brain injury prognosis. Seeing SDs with a new dimension - frequency on spectrograms, we demonstrated that such additional dimension could improve SD detection accuracy, providing preliminary evidence to support the hypothesis that SDs may show implicit features over the frequency profile.

Signatures of migraine aura in high-density-EEG

OBJECTIVE

Cortical spreading depolarization is highly conserved among the species. It is easily detectable in direct cortical surface recordings and has been recorded in the cortex of humans with severe neurological disease. It is considered the pathophysiological correlate of human migraine aura, but direct electrophysiological evidence is still missing. As signatures of cortical spreading depolarization have been recognized in scalp EEG, we investigated typical spontaneous migraine aura, using full band high-density EEG (HD-EEG).

METHODS

In this prospective study, patients with migraine with aura were investigated during spontaneous migraine aura and interictally. Time compressed HD-EEG were analyzed for the presence of cortical spreading depolarization characterized by (a) slow potential changes below 0.05 Hz, (b) suppression of faster activity from 0.5 Hz - 45 Hz (c) spreading of these changes to neighboring regions during the aura phase. Further, topographical changes in alpha-power spectral density (8-14 Hz) during aura were analyzed.

RESULTS

In total, 26 HD-EEGs were recorded in patients with migraine with aura, thereof 10 HD-EEGs during aura. Eight HD-EEGs were recorded in the same subject. During aura, no slow potentials were recorded, but alpha-power was significantly decreased in parieto-occipito-temporal location on the hemisphere contralateral to visual aura, lasting into the headache phase. Interictal alpha-power in patients with migraine with aura did not differ significantly from age- and sex-matched healthy controls.

CONCLUSIONS

Unequivocal signatures of spreading depolarization were not recorded with EEG on the intact scalp in migraine. The decrease in alpha-power contralateral to predominant visual symptoms is consistent with focal depression of spontaneous brain activity as a consequence of cortical spreading depolarization but is not specific thereof.

SIGNIFICANCE

Cortical spreading depolarization is relevant in migraine, other paroxysmal neurological disorders and neurointensive care.

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.

Oxyhemoglobin and Cerebral Blood Flow Transients Detect Infarction in Rat Focal Brain Ischemia

Spreading depolarizations (SD) refer to the near-complete depolarization of neurons that is associated with brain injuries such as ischemic stroke. The present gold standard for SD monitoring in humans is invasive electrocorticography (ECoG). A promising non-invasive alternative to ECoG is diffuse optical monitoring of SD-related flow and hemoglobin transients. To investigate the clinical utility of flow and hemoglobin transients, we analyzed their association with infarction in rat focal brain ischemia. Optical images of flow, oxy-hemoglobin, and deoxy-hemoglobin were continuously acquired with Laser Speckle and Optical Intrinsic Signal imaging for 2 h after photochemically induced distal middle cerebral artery occlusion in Sprague-Dawley rats (n = 10). Imaging was performed through a 6 × 6 mm window centered 3 mm posterior and 4 mm lateral to Bregma. Rats were sacrificed after 24 h, and the brain slices were stained for assessment of infarction. We mapped the infarcted area onto the imaging data and used nine circular regions of interest (ROI) to distinguish infarcted from non-infarcted tissue. Transients propagating through each ROI were characterized with six parameters (negative, positive, and total amplitude; negative and positive slope; duration). Transients were also classified into three morphology types (positive monophasic, biphasic, negative monophasic). Flow transient morphology, positive amplitude, positive slope, and total amplitude were all strongly associated with infarction (p < 0.001). Associations with infarction were also observed for oxy-hemoglobin morphology, oxy-hemoglobin positive amplitude and slope, and deoxy-hemoglobin positive slope and duration (all p < 0.01). These results suggest that flow and hemoglobin transients accompanying SD have value for detecting infarction.

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Traumatic brain injury (TBI) can produce temporary biochemical imbalance due to leaks through cell membranes or disruption of the axoplasmic flow due to the misalignment of intracellular neurofilaments. If untreated, TBI can lead to Alzheimer's, Parkinson's, or total disability. Mild TBI (mTBI) accounts for about about 90 percent of all TBI cases. The detection of TBI as soon as it happens is crucial for successful treatment management. Neuroimaging-based tests provide only a structural and functional mapping of the brain with poor temporal resolution. Such tests may not detect mTBI. On the other hand, the electroencephalogram (EEG) provides good spatial resolution and excellent temporal resolution of the brain activities beside its portability and low cost. The objective of this paper is to provide clinicians and scientists with a one-stop source of information to quickly learn about the different technologies used for TBI detection, their advantages and limitations. Our research led us to conclude that even though EEG-based TBI detection is potentially a powerful technology, it is currently not able to detect the presence of a mTBI with high confidence. The focus of the paper is to review existing approaches and provide the reason for the unsuccessful state of EEG-based detection of mTBI.

Numerical Simulation of Concussive-Generated Cortical Spreading Depolarization to Optimize DC-EEG Electrode Spacing for Noninvasive Visual Detection

BACKGROUND

Cortical spreading depolarization (SD) is a propagating depolarization wave of neurons and glial cells in the cerebral gray matter. SD occurs in all forms of severe acute brain injury, as documented by using invasive detection methods. Based on many experimental studies of mechanical brain deformation and concussion, the occurrence of SDs in human concussion has often been hypothesized. However, this hypothesis cannot be confirmed in humans, as SDs can only be detected with invasive detection methods that would require either a craniotomy or a burr hole to be performed on athletes. Typical electroencephalography electrodes, placed on the scalp, can help detect the possible presence of SD but have not been able to accurately and reliably identify SDs.

METHODS

To explore the possibility of a noninvasive method to resolve this hurdle, we developed a finite element numerical model that simulates scalp voltage changes that are induced by a brain surface SD. We then compared our simulation results with retrospectively evaluated data in patients with aneurysmal subarachnoid hemorrhage from Drenckhahn et al. (Brain 135:853, 2012).

RESULTS

The ratio of peak scalp to simulated peak cortical voltage, Vscalp/Vcortex, was 0.0735, whereas the ratio from the retrospectively evaluated data was 0.0316 (0.0221, 0.0527) (median [1st quartile, 3rd quartile], n = 161, p < 0.001, one sample Wilcoxon signed-rank test). These differing values provide validation because their differences can be attributed to differences in shape between concussive SDs and aneurysmal subarachnoid hemorrhage SDs, as well as the inherent limitations in human study voltage measurements. This simulated scalp surface potential was used to design a virtual scalp detection array. Error analysis and visual reconstruction showed that 1 cm is the optimal electrode spacing to visually identify the propagating scalp voltage from a cortical SD. Electrode spacings of 2 cm and above produce distorted images and high errors in the reconstructed image.

CONCLUSIONS

Our analysis suggests that concussive (and other) SDs can be detected from the scalp, which could confirm SD occurrence in human concussion, provide concussion diagnosis on the basis of an underlying physiological mechanism, and lead to noninvasive SD detection in the setting of severe acute brain injury.

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

BACKGROUND

Spreading depolarizations (SDs) are associated with worse outcome following subarachnoid hemorrhage (SAH) and traumatic brain injury (TBI), but gold standard detection requires electrocorticography with a subdural strip electrode. Electroencephalography (EEG) ictal-interictal continuum abnormalities are associated with poor outcomes after TBI and with both delayed cerebral ischemia (DCI) and poor outcomes after SAH. We examined rates of SD detection in patients with SAH and TBI with intraparenchymal and subdural strip electrodes and assessed which continuous EEG (cEEG) measures were associated with intracranially quantified SDs.

METHODS

In this single-center cohort, we included patients with SAH and TBI undergoing ≥ 24 h of interpretable intracranial monitoring via eight-contact intraparenchymal or six-contact subdural strip platinum electrodes or both. SDs were rated according to established consensus criteria and compared with cEEG findings rated according to the American Clinical Neurophysiology Society critical care EEG monitoring consensus criteria: lateralized rhythmic delta activity, generalized rhythmic delta activity, lateralized periodic discharges, generalized periodic discharges, any ictal-interictal continuum, or a composite scalp EEG tool for seizure risk estimation: the 2HELPS2B score. Among patients with SAH, cEEG was assessed for validated DCI biomarkers: new or worsening epileptiform abnormalities and new background deterioration.

RESULTS

Over 6 years, SDs were recorded in 5 (18%) of 28 patients recorded with intraparenchymal electrodes and 4 (40%) of 10 patients recorded with subdural strip electrodes. There was no significant association between occurrence of SDs and day 1 cEEG findings (American Clinical Neurophysiology Society main terms lateralized periodic discharges, generalized periodic discharges, lateralized rhythmic delta activity, or seizures, individually or in combination). After SAH, established cEEG DCI predictors were not associated with SDs.

CONCLUSIONS

Intraparenchymal recordings yielded low rates of SD, and documented SDs were not associated with ictal-interictal continuum abnormalities or other cEEG DCI predictors. Identifying scalp EEG correlates of SD may require training computational EEG analytics and use of gold standard subdural strip electrocorticography recordings.

Neural silences can be localized rapidly using noninvasive scalp EEG

A rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit

OBJECTIVE

New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification.

METHODS

IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (PbtO2), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application.

RESULTS

Sustained increases in ICP and concordant decreases in PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r2 0.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%.

CONCLUSION

This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers.

SIGNIFICANCE

This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.

A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System

A leading cause of traumatic brain injury (TBI) is intracranial brain deformation due to mechanical impact. This deformation is viscoelastic and differs from a traditional rigid transformation. In this paper, we describe a machine learning enabled wireless sensing system that predicts the trajectory of intracranial brain deformation. The sensing system consists of an implantable soft magnet and an external magnetic sensor array with a sensing volume of 12 × 12 × 4 mm³. Machine learning algorithm predicts the brain deformation by interpreting the magnetic sensor outputs created by the change in position of the implanted soft magnet. Three different machine learning models were trained on calibration data: (1) random forests, (2) k-nearest neighbors, and (3) a multi-layer perceptron-based neural network. These models were validated using both in vitro (a needle inserted into PVC gel) and in vivo (blast exposure to live and dead rat brains) experiments. The in vitro gel deformation predicted by these machine learning models showed excellent agreement with the camera measurements and had absolute error = 138 μm, Fréchet distance = 372 μm with normalized Procrustes disparity = 0.034. The in vivo brain deformation predicted by these models had absolute error = 50 μm, Fréchet distance = 95 μm with normalized Procrustes disparity = 0.055 for dead animal and absolute error = 125 μm, Fréchet distance = 289 μm with normalized Procrustes disparity = 0.2 for live animal respectively. These results suggest that the proposed machine learning enabled sensor system can be an effective tool for measuring in situ brain deformation.

An Algorithm for Automated, Noninvasive Detection of Cortical Spreading Depolarizations Based on EEG Simulations

OBJECTIVE

We present a novel signal processing algorithm for automated, noninvasive detection of cortical spreading depolarizations (CSDs) using electroencephalography (EEG) signals and validate the algorithm on simulated EEG signals. CSDs are waves of neurochemical changes that suppress the neuronal activity as they propagate across the brain's cortical surface. CSDs are believed to mediate secondary brain damage after brain trauma and cerebrovascular diseases like stroke. We address the following two key challenges in detecting CSDs from EEG signals: i) attenuation and loss of high spatial resolution information; and ii) cortical folds, which complicate tracking CSD waves.

METHODS

Our algorithm detects and tracks "wavefronts" of a CSD wave, and stitch together data across space and time to make a detection. To test our algorithm, we provide different models of CSD waves, including different widths of CSD suppressions and different patterns, and use them to simulate scalp EEG signals using head models of four subjects.

RESULTS AND CONCLUSION

Our results suggest that low-density EEG grids (40 electrodes) can detect CSD widths of 1.1 cm on average, while higher density EEG grids (340 electrodes) can detect CSD patterns as thin as 0.43 cm (less than minimum widths reported in prior works), among which single-gyrus CSDs are the hardest to detect because of their small suppression area.

SIGNIFICANCE

The proposed algorithm is a first step toward noninvasive, automated detection of CSDs, which can help in reducing secondary brain damages.