| Summary: | Unravelling the riddles of the constant interplay between modulatory pathways between the brain and the body is important for various applications in research and clinical fields. Unveiling how information related to autonomic regulation is encoded in the brain can be translated into a better understanding of emotional and physiological traits by investigating the recorded neural signals.
Previous studies show heart and respiratory coupling with neural spectral patterns, suggesting heart rate variability (HRV) can be decoded from brain signals. This study aimed at decoding HRV states, particularly its low frequency (LF) component, using the spectral content in neural signals recorded with magnetoencephalography (MEG) during so called resting state. The data collection involved 33 participants, with electrocardiogram (ECG) signals recorded simultaneously with MEG. This study's novelty lies in a data-driven segmentation approach to epoch MEG and ECG signals according to natural HRV fluctuations.
Eighteen variables were assessed in a logistic regression machine learning (ML) model, where each MEG segment was a sample, and model features consisted of MEG power across five frequency bands (delta, theta, alpha, beta, and gamma). The variables included three respiratory patterns (Deep breathing with eyes open, spontaneous breathing with eyes closed, spontaneous breathing with eyes open) × three MEG sensor types (magnetometers, gradiometers, or both) × two ECG segmentation methods (increasing and decreasing HRV gradient trends, or high and low HRV segments). The decodability of HRV variability from MEG data could not be confirmed since models performed at chance level. Future research suggestions are provided to improve the results.
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