Electroencephalography (EEG) microstates analysis provides a sequence of topographies representing the scalp-related electric field over time, and each microstate is synthetically represented by a symbol. Despite recent advances on functional brain-heart interplay (BHI) assessment, to our knowledge no methodology takes EEG microstates into account to relate the causal heartbeat dynamics. Moreover, standard BHI methods are tailored to a single EEG-channel analysis, neglecting the comprehensive information associated with a multichannel cluster or a whole-brain activity. To overcome these limitations, we devised a novel methodological frame-work for the assessment of functional BHI that exploits the symbolic representation of both EEG microstates and heart rate variability (HRV) series. Directional BHI quantification is then performed through Kullback-Leibler Divergence (KLD) and Transfer Entropy. The proposed methodology is here preliminarily tested on a dataset gathered from healthy subjects undergoing a resting state and a mental arithmetic task. Except for the KLD in the from-brain-to-heart direction, all other estimates showed significant differences between the two experimental conditions. We conclude that the proposed frame-work may promisingly provide novel insights on brain-heart phenomena through a whole-brain symbolic representation.

Causal Symbolic Information Transfer for the Assessment of functional Brain-Heart Interplay through EEG Microstates Occurrences: a proof-of-concept study

Catrambone V.;Valenza G.
2022-01-01

Abstract

Electroencephalography (EEG) microstates analysis provides a sequence of topographies representing the scalp-related electric field over time, and each microstate is synthetically represented by a symbol. Despite recent advances on functional brain-heart interplay (BHI) assessment, to our knowledge no methodology takes EEG microstates into account to relate the causal heartbeat dynamics. Moreover, standard BHI methods are tailored to a single EEG-channel analysis, neglecting the comprehensive information associated with a multichannel cluster or a whole-brain activity. To overcome these limitations, we devised a novel methodological frame-work for the assessment of functional BHI that exploits the symbolic representation of both EEG microstates and heart rate variability (HRV) series. Directional BHI quantification is then performed through Kullback-Leibler Divergence (KLD) and Transfer Entropy. The proposed methodology is here preliminarily tested on a dataset gathered from healthy subjects undergoing a resting state and a mental arithmetic task. Except for the KLD in the from-brain-to-heart direction, all other estimates showed significant differences between the two experimental conditions. We conclude that the proposed frame-work may promisingly provide novel insights on brain-heart phenomena through a whole-brain symbolic representation.
2022
978-1-7281-2782-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1156362
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