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Exploring neurovascular coupling in stroke patients: insights on linear and nonlinear dynamics using transfer entropy

par Rozo, Andrea ;Hasan, Shafiul;Zhang, Zhe;Iorio, Carlo Saverio ;Varon, Carolina ;Hu, Xiao
Référence Journal of neural engineering, 22, 3, page (036009)
Publication Publié, 2025-05-08

Article révisé par les pairs

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Résumé :

Abstract Objective. The study of neurovascular coupling (NVC), the relationship between neuronal activity and cerebral blood flow, is essential for understanding brain physiology in both healthy and pathological states. Current methods to study NVC include neuroimaging techniques with limited temporal resolution and indirect neuronal activity measures. Methods including electroencephalographic (EEG) data are predominantly linear and display limitations that nonlinear methods address. Transfer entropy (TE) explores linear and nonlinear relationships simultaneously. This study hypothesizes that complex NVC interactions in stroke patients, both linear and nonlinear, can be detected using TE. Approach. TE between simultaneously recorded EEG and cerebral blood flow velocity (CBFV) signals was computed and analyzed in three settings: ipsilateral (EEG and CBFV from same hemisphere) stroke and nonstroke, and contralateral (EEG from stroke hemisphere, CBFV from nonstroke hemisphere). A surrogate analysis was performed to evaluate the significance of TE values and to identify the nature of the interactions. Main results. The results showed that EEG generally influenced CBFV. There were more linear+nonlinear interactions in the ipsilateral nonstroke setting and in the delta band in ipsilateral stroke and contralateral settings. Interactions between EEG and CBFV were stronger on the nonstroke side for linear+nonlinear dynamics. The strength and nature of the interactions were weakly correlated with clinical outcomes (e.g. delta band ( p < 0.05): infarct growth linear = −0.448, linear+nonlinear = −0.339; NIHSS linear = −0.473, linear+nonlinear = −0.457). Significance. This study exemplifies the benefits of using TE in linear and nonlinear NVC analysis to better understand the implications of these dynamics in stroke severity.