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Cross-Modal Multivariate Pattern Analysis
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Neurovascular Coupling Analysis Based on Multivariate Variational Gaussian Process Convergent Cross-Mapping.

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    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 8, 2024
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    This summary is machine-generated.

    A new method, CMVGP-CCM, analyzes brain activity coupling between electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). It reveals EEG

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Cognitive Science

    Background:

    • Neurovascular coupling (NVC) is crucial for understanding brain function and disease diagnosis.
    • Assessing NVC using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is challenging due to a lack of standardized methods.
    • Reliable techniques for analyzing the coupling between EEG and fNIRS signals are needed.

    Purpose of the Study:

    • To introduce a novel method, collaborative multi-output variational Gaussian process convergent cross-mapping (CMVGP-CCM), for analyzing EEG-fNIRS coupling.
    • To validate the CMVGP-CCM method's robustness and reliability.
    • To explore NVC during a working memory (WM) task using EEG and fNIRS signals.

    Main Methods:

    • Developed and validated the CMVGP-CCM approach using chaotic time series models with varying noise levels, sequence lengths, and causal driving strengths.
    • Applied the CMVGP-CCM method to analyze EEG and fNIRS data from 26 healthy participants performing a working memory task.
    • Investigated the causal influence of specific EEG frequency bands (delta, theta, alpha) on fNIRS signals in the frontal lobe.

    Main Results:

    • The CMVGP-CCM method demonstrated robustness and reliability in analyzing time series data.
    • EEG signals, particularly delta, theta, and alpha bands, significantly influenced fNIRS signals during the working memory task.
    • This EEG-to-fNIRS influence was prominent in the frontal lobe and decreased with increased cognitive demand.

    Conclusions:

    • The CMVGP-CCM approach provides a reliable method for analyzing EEG-fNIRS coupling.
    • EEG activity causally influences cerebral blood flow dynamics, as measured by fNIRS, during working memory tasks.
    • This study offers new insights into the neurovascular mechanisms underlying working memory and brain electrical activity-blood flow interactions.