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

Background Following stroke, brain networks can be described by strength of local connections (clustering coefficient [ C w]) and strength of global interconnections (path length [ L w]) between nodes, and their balance (Small-worldness [ S w]). Objective . To identify electroencephalography (EEG) networks predicting clinical evolution in stroke through a multicenter cross-sectional study. Methods We consecutively recruited 87 anterior circulation ischemic stroke patients. We obtained resting-state EEG (31 electrodes, 10-10 system) within 24 hours from stroke ( T 0) and at discharge from stroke unit (4–10 days after stroke; T 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 T 0 and T 1 as dependent variables and C w, L w, and S w of delta, theta, and alpha networks as independent variables. Results We found a negative association between alpha1 S w and NIHSS at T 0 (β = −.232, P = .04) meaning that the lower is alpha efficiency the higher is clinical severity and a positive association between delta S w and NIHSS at T 1 (β = .423, P < .001) meaning that the higher is delta efficiency the higher is clinical severity. We found positive association between delta S w at T 0 and NIHSS at T 1 (β = .259, P = .02), meaning that the higher is delta efficiency in the hyperacute phase the higher is clinical severity at T 1. Conclusions A higher delta S 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.