Electroencephalography resting‐state networks in people with Stroke

Abstract

Introduction The purpose of this study was to characterize resting‐state cortical networks in chronic stroke survivors using electroencephalography (EEG). Methods Electroencephalography data were collected from 14 chronic stroke and 11 neurologically intact participants while they were in a relaxed, resting state. EEG power was normalized to reduce bias and used as an indicator of network activity. Correlations of orthogonalized EEG activity were used as a measure of functional connectivity between cortical regions. Results We found reduced cortical activity and connectivity in the alpha (p < .05; p = .05) and beta (p < .05; p = .03) bands after stroke while connectivity in the gamma (p = .031) band increased. Asymmetries, driven by a reduction in the lesioned hemisphere, were also noted in cortical activity (p = .001) after stroke. Conclusion These findings suggest that stroke lesions cause a network alteration to more local (higher frequency), asymmetric networks. Understanding changes in cortical networks after stroke could be combined with controllability models to identify (and target) alternate brain network states that reduce functional impairment.

Combination of Group Singular Value Decomposition and eLORETA Identifies Human EEG Networks and Responses to Transcranial Photobiomodulation

“…Custo et al in 2017 applied k-mean clustering to classify EEG temporal topographies and applied the source localization algorithm to identify respective cortical sources ( Custo et al, 2017 ). Snyder et al more recently reported brain network functional impairment in stroke patients with respect to healthy participants using 3D volumetric, orthogonalized EEG data analysis ( Snyder et al, 2021 ). The reason of orthogonalizing the EEG time courses was to reduce the effect of volume conduction on connectivity analyses ( Snyder et al, 2021 ).…”

“…Snyder et al more recently reported brain network functional impairment in stroke patients with respect to healthy participants using 3D volumetric, orthogonalized EEG data analysis ( Snyder et al, 2021 ). The reason of orthogonalizing the EEG time courses was to reduce the effect of volume conduction on connectivity analyses ( Snyder et al, 2021 ). Along the same direction or argument, we explored a novel approach by combining group singular value decomposition (gSVD) and eLORETA to identify human EEG brain networks and responses to tPBM.…”

Functional Brain Controllability Alterations in Stroke

“…Recently, advanced neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG), have been widely employed to investigate the dynamic alteration of cortical excitability and network connectivity following stroke, and shown great potential to understand the relationship between the dysfunctional brain network and motor control deficits ( Grefkes et al, 2008a ; Bajaj et al, 2014 ; Snyder et al, 2021 ). For example, previous fMRI study illustrated that the motor control deficits of stroke patients were associated with pathological intra- and inter-hemispheric interactions among key motor regions such as primary motor cortex (M1), premotor cortex (PMC), and supplementary motor cortex (SMA), and executive control network (ECN) ( Grefkes et al, 2008a ; Zhao et al, 2018 ).…”

StrokeSight: A Novel EEG-Based Diagnostic System for Strokes Using Spectral Analysis and Deep Learning

“…EEGs have been shown to contain many biomarkers of strokes that can be used as a diagnostic basis. It has been shown that people who have suffered a stroke display reduced cortical activity and connectivity in both the alpha and beta waves and an increase in activity in the gamma wave [10]. These abnormalities are driven by the drop in cerebral blood flow to brain tissue during a stroke.…”

“…These abnormalities are driven by the drop in cerebral blood flow to brain tissue during a stroke. Furthermore, asymmetries in activity will likely be seen as a reduction of activity in the affected hemisphere [10]. EEGs could thus be a powerful method for fast and remote stroke diagnosis, but no system currently exists that leverages EEGs to return a full personalized patient diagnosis.…”