Assessment of Neurovascular Coupling in Stroke using Transfer Entropy

Understanding the complex dynamics of neurovascular coupling (NVC), especially in stroke, is crucial for improving patient care and treatment. A model-free method is essential for analyzing NVC due to its potential linear or nonlinear dynamics. This study aimed to explore the use of transfer entropy (TE) to identify NVC dynamics between electroencephalogram (EEG) and cerebral blood flow velocity (CBFV) signals in stroke. Data from 52 stroke patients were used to compute the TE from EEG to CBFV and vice versa in 5-minute windows. Three settings were analyzed: ipsilateral stroke, ipsilateral nonstroke, and contralateral. In all settings, changes in EEG were found to drive changes in CBFV. Additionally, the percentage of significant windows was correlated with the modified Rankin score. TE appears suitable for further understanding the implications of stroke in NVC dynamics.