Cortical Network Topological Modifications Underlie Clinical Evolution in the Acute Phase of Ischemic Stroke.

BackgroundFollowing stroke, brain networks can be described by strength of local connections (clustering coefficient [w]) and strength of global interconnections (path length [w]) between nodes, and their balance (Small-worldness [w]). . To identify electroencephalography (EEG) networks predicting clinical evolution in stroke through a multicenter cross-sectional study.MethodsWe consecutively recruited 87 anterior circulation ischemic stroke patients. We obtained resting-state EEG (31 electrodes, 10-10 system) within 24 hours from stroke (0) and at discharge from stroke unit (4-10 days after stroke; 1). EEG data were elaborated with EEGLAB and Lagged Linear Coherence among cortical sources of EEG signals was analyzed using eLORETA. We performed a multiple linear regression with National Institutes of Health Stroke Scale (NIHSS) at 0 and 1 as dependent variables and w, w, and w of delta, theta, and alpha networks as independent variables.ResultsWe found a negative association between alpha1 w and NIHSS at 0 (β = -.232,  = .04) meaning that the lower is alpha efficiency the higher is clinical severity and a positive association between delta w and NIHSS at 1 (β = .423,  < .001) meaning that the higher is delta efficiency the higher is clinical severity. We found positive association between delta w at 0 and NIHSS at 1 (β = .259,  = .02), meaning that the higher is delta efficiency in the hyperacute phase the higher is clinical severity at 1.ConclusionsA higher delta w within 24 hours after stroke is associated to higher NIHSS within 10 days. Delta brain network rearrangement in the hyperacute phase is a potential neurophysiological measure to be integrated in multi-modal prognostic models.