Dynamic brain network modulation by paced breathing and breath-holding: an EEG-based functional connectivity study

Area of Science:

• Neuroscience
• Respiratory Physiology
• Computational Neuroscience

Background:

• Voluntary breathing maneuvers are known to modulate cortical oscillations.
• The precise frequency-specific signatures of brain functional connectivity (FC) during different respiratory states remain unclear.

Purpose of the Study:

• To investigate the impact of distinct respiratory conditions (self-paced breathing, breath-holding, computer-paced breathing) on brain FC.
• To analyze frequency-specific changes in brain network organization and identify potential biomarkers for respiratory conditions.

Main Methods:

• Electroencephalography (EEG) recordings were used to capture brain activity during three respiratory conditions.
• Analysis included power spectral density (PSD), phase-locking value (PLV), and brain network characteristics (clustering coefficient, global efficiency, local efficiency, degree centrality).

Main Results:

• Significant differences in PSD were observed, with increased low-frequency activity during self-paced breathing and higher high-frequency activity during breath-holding.
• PLV analysis revealed significant modulation of brain networks by respiratory state.
• Brain network properties showed frequency-specific alterations, and an alpha-band classifier demonstrated high performance in distinguishing respiratory conditions. Associations between brain connectivity and forced vital capacity (FVC) were found.

Conclusions:

• Breathing conditions intricately influence brain functional connectivity and network organization.
• Controlled and uncontrolled breathing patterns impact neural organization, suggesting potential for respiratory-based interventions to enhance cognitive function.