Universal Lifespan Trajectories of Source-Space Information Flow Extracted from Resting-State MEG Data

Area of Science:

• Neuroscience
• Brain Connectivity
• Information Theory

Background:

• Resting-state magnetoencephalography (MEG) provides insights into brain dynamics.
• Understanding information flow directionality and coupling modes is crucial for deciphering brain function.

Purpose of the Study:

• To analyze the directionality, strength, and time delays of causal interactions in the human brain.
• To compute the dominant intrinsic coupling mode (DoCM) independently for phase and amplitude.
• To introduce and validate a novel brain age index (BAI) based on neural coupling patterns.

Main Methods:

• Extracted source activity from resting-state MEG data of 103 subjects (18-60 years).
• Utilized delay symbolic transfer entropy and phase entropy to compute information flow directionality.
• Calculated DoCM and introduced a novel brain age index (BAI) based on inter- and intra-frequency couplings.

Main Results:

• Identified distinct posterior-to-anterior and anterior-to-posterior information flow patterns for phase and amplitude dynamics across various frequency bands (δ, θ, α1, α2, β, γ).
• Reported DoCM for intra- and cross-frequency couplings (CFC) for the first time, highlighting {δ, θ, α1} as key contributors.
• Introduced a novel brain age index (BAI) exhibiting a universal age trajectory: rising from adolescence, peaking in adulthood, and declining thereafter, consistent across frequencies and domains.

Conclusions:

• The study elucidates complex directional information flow and coupling modes in the brain.
• The novel brain age index (BAI) provides a robust, universal marker for tracking age-related changes in neural connectivity.
• These findings offer new perspectives on brain development, aging, and functional organization.