Abstract
Neural oscillations may contain important information pertaining to stroke rehabilitation. This study examined the predictive performance of electroencephalography‐derived neural oscillations following stroke using a data‐driven approach. Individuals with stroke admitted to an inpatient rehabilitation facility completed a resting‐state electroencephalography recording and structural neuroimaging around the time of admission and motor testing at admission and discharge. Using a lasso regression model with cross‐validation, we determined the extent of motor recovery (admission to discharge change in Functional Independence Measurement motor subscale score) prediction from electroencephalography, baseline motor status, and corticospinal tract injury. In 27 participants, coherence in a 1–30 Hz band between leads overlying ipsilesional primary motor cortex and 16 leads over bilateral hemispheres predicted 61.8% of the variance in motor recovery. High beta (20–30 Hz) and alpha (8–12 Hz) frequencies contributed most to the model demonstrating both positive and negative associations with motor recovery, including high beta leads in supplementary motor areas and ipsilesional ventral premotor and parietal regions and alpha leads overlying contralesional temporal–parietal and ipsilesional parietal regions. Electroencephalography power, baseline motor status, and corticospinal tract injury did not significantly predict motor recovery during hospitalization (R 2 = 0–6.2%). Findings underscore the relevance of oscillatory synchronization in early stroke rehabilitation while highlighting contributions from beta and alpha frequency bands and frontal, parietal, and temporal–parietal regions overlooked by traditional hypothesis‐driven prediction models.
Highlights
Stroke is the leading neurological cause of disability in the United States (Feigin et al, 2021) with motor deficits constituting a significant source of poststroke burden
Based on past work underscoring the significance of resting-state oscillations in alpha (Dubovik et al, 2013), delta (Cassidy et al, 2020), theta (Saes et al, 2020), and beta (Wu et al, 2015) frequency bands along with functional connections with ipsilesional primary motor cortex spanning both ipsi- and contralesional hemispheres, lasso regression is an appropriate strategy for the prediction of motor recovery during poststroke hospitalization that may reveal additional findings not otherwise apparent through mainly traditional hypothesis-driven approaches
Despite collinearity likely existing between baseline motor status (UEFM) and motor recovery, we examined the amount of variance in motor recovery explained by baseline motor status and found it to be nonsignificant (R2 = 1.1%, p = .54)
Summary
Stroke is the leading neurological cause of disability in the United States (Feigin et al, 2021) with motor deficits constituting a significant source of poststroke burden. Our prior EEG work in subacute stroke found (1) negative associations between beta power in leads overlying ipsilesional sensorimotor and contralesional parietal cortices with motor impairment and global stroke severity status (Wu et al, 2016), (2) positive associations between delta power in ipsilesional sensorimotor and contralesional frontoparietal cortices (Wu et al, 2016) with motor impairment and global stroke severity status, and (3) reductions in delta coherence between bilateral primary motor cortices paralleling motor recovery (Cassidy et al, 2020) Informed by these findings, traditional hypothesis-driven prediction models for early stroke motor recovery would contain contributions from both high- and low-frequency bands in leads overlying bilateral primary and secondary motor regions in early stroke motor recovery. Based on past work underscoring the significance of resting-state oscillations in alpha (Dubovik et al, 2013), delta (Cassidy et al, 2020), theta (Saes et al, 2020), and beta (Wu et al, 2015) frequency bands along with functional connections with ipsilesional primary motor cortex (iM1) spanning both ipsi- and contralesional hemispheres, lasso regression is an appropriate strategy for the prediction of motor recovery during poststroke hospitalization that may reveal additional findings not otherwise apparent through mainly traditional hypothesis-driven approaches
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