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bioRxiv · 10.1101/504183

Embedding the Skin Conductance Response into the Brain Connectivity Framework: Monoaminergic Signaling Visible Through the Lenses of Computational Modeling

Abstract

Relying on evidence for the functional, neurochemical, and spectral parallelism between the late event-related potentials, delta oscillatory brain responses, and the skin conductance response (SCR) system the hypotheses about the existence of the SCR-related brain oscillations and their connectivity with the SCR system have been here suggested. In contrast to classical approach to event-related oscillations which relies on either stimulus- or response-locked time reference, an approach assigned as "oscillatory process-related oscillations" has been introduced. The method enables us to overcome the variability of latency period of the SCR. The hypothesis about the existence of the SCR-related brain oscillations and their delta nature has been confirmed through the grand averaging method. An unexpected finding was the complex nature of the SCR-related oscillations: in addition to the two second EEG segment which was correlated with the SCR system signals they also comprised an initial 200 ms segment uncorrelated with the SCR. The hypothesis about the connectivity between the SCR system and the respective delta brain oscillatory response has been operationalized through a multiple time series regression model. The predictor set consists of the SCR, its first three derivatives, and their mutual interactions. The Monte Carlo test of the causal link between the SCR system signals and the related delta EEG signal demonstrated significance in more than half of the participants. The findings have been considered from the standpoints of the segmental structure of the EEG, monoaminergic signaling and recently emerged the "brain-body dynamic syncytium" hypothesis.

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BibTeXRIS

Brankovic, S.. 2018-12-21. Embedding the Skin Conductance Response into the Brain Connectivity Framework: Monoaminergic Signaling Visible Through the Lenses of Computational Modeling. https://doi.org/10.1101/504183

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