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

Source activity was extracted from resting-state magnetoencephalography data of 103 subjects aged 18-60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling the direction, strength, and time delay of the underlying causal interactions, producing independent time delays for cross-frequency amplitude-to-amplitude and phase-to-phase coupling. The computation of the dominant intrinsic coupling mode (DoCM) allowed me to estimate the probability distribution of the DoCM independently of phase and amplitude. The results support earlier observations of a posterior-to-anterior information flow for phase dynamics in {alpha(1), alpha(2), beta, gamma} and an opposite flow (anterior to posterior) in theta. Amplitude dynamics reveal posterior-to-anterior information flow in {alpha(1), alpha(2), gamma}, a sensory-motor beta-oriented pattern, and an anterior-to-posterior pattern in {delta, theta}. The DoCM between intra- and cross-frequency couplings (CFC) are reported here for the first time and independently for amplitude and phase; in both domains {delta, theta, alpha(1)}, frequencies are the main contributors to DoCM. Finally, a novel brain age index (BAI) is introduced, defined as the ratio of the probability distribution of inter- over intra-frequency couplings. This ratio shows a universal age trajectory: a rapid rise from the end of adolescence, reaching a peak in adulthood, and declining slowly thereafter. The universal pattern is seen in the BAI of each frequency studied and for both amplitude and phase domains. No such universal age dependence was previously reported.

Automated summary

Resting-state magnetoencephalography data of 103 subjects aged 18-60 years were analyzed to uncover the directionality and time delay of information flow between brain regions. Additionally, a novel brain age index was introduced, showing the age-dependent changes in brain activity across different frequencies.