A novel neurovascular coupling analysis method based on simultaneous EEG-fNIRS for investigating the effects of working memory load.

Authors

Yu Wang, Qi Pan, Bin Wen, Qiong Wu, Qiumin Qu, Jilun Ye, Xu Zhang, Jin Xu

Published in

Medical & biological engineering & computing. Jul 30, 2026. Epub Jul 30, 2026.

Abstract

The relationship between neural activities and hemodynamic responses was referred to as neurovascular coupling (NVC). Although several methodologies were proposed for the NVC study, these approaches may be constrained in extracting event-related neural activities (ERNA) and lacking the characterization of NVC. To optimize the extraction of ERNA, the modified connectivity-related neural activity (CRNA) was extracted by combining the absolute value of neural oscillation with non-negative matrix factorization (NNMF). Then, cross-correlation between modified CRNA and hemoglobin oxygenation (HbO) was computed to represent NVC, and temporal and intensity parameters of NVC were extracted, respectively. A digit-Sternberg task was designed with three different levels for investigating effects of working memory load (WML) on NVC. Electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS)were recorded simultaneously in 30 healthy elderly adults. We observed that as WML increased, NVC intensity at prefrontal and parietal regions exhibited a notable enhancement, while NVC lag exhibited a significant decrease. Furthermore, a significant negative correlation was found between NVC intensity and reaction time (RT). The proposed NVC analysis method could effectively characterize the influence of WML changes on NVC.

PMID:
42530784
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.

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