Brain-scale theta band functional connectome as signature of slow breathing and breath-hold phases.

The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold. The variations in the electroencephalogram (EEG) based functional connectivity (FC) of the human brain have been investigated during attentive breathing at 2 cycles per minute (cpm). The study presents its novelty through three main aspects. First, it explores the complex breathing circuitry beyond the brain stem, specifically examining how higher brain regions interact with respiratory cycles. Second, unlike previous studies that treated respiratory phases as a singular phenomenon, this research analyses inhalation, exhalation, and breath-holds separately, providing a deeper understanding of their individual dynamics and FC in the brain. Finally, the breathing protocol is designed to include inhale-hold and exhale-hold sessions alongside symmetric breathing, allowing for testing on healthy subjects rather than specialized cohorts, which were used in earlier studies. An experimental protocol involving equal durations of inhale, inhale-hold, exhale, and exhale-hold conditions, synchronized to a visual metronome, was designed and administered to 20 healthy subjects (9 females and 11 males, age: 32.0 ± 9.5 years (mean ± SD)). EEG data were collected during these sessions using the 64-channel eego™ mylab system from ANT Neuro. Further, FC was estimated for all possible pairs of EEG time series data, for 7 EEG bands. Feature selection using a genetic algorithm (GA) was performed to identify a subset of functional connections that would best distinguish the inhale, inhale-hold, exhale, and exhale-hold phases using a random committee classifier. The best accuracy of 95.056% was obtained when 403 theta-band functional connections were fed as input to the classifier, highlighting the efficacy of the theta-band functional connectome in distinguishing these phases of the respiratory cycle. This functional network was further characterized using graph measures, and observations illustrated a statistically significant difference in the efficiency of information exchange through the network during different respiratory phases.