Back

Frequency-resolved cortical functional connectivityacross the adult lifespan

Ruuskanen, S.; Avendano-Diaz, J. C.; Liljeström, M.; Parkkonen, L.

2026-01-14 neuroscience
10.1101/2025.03.28.645908 bioRxiv
Show abstract

The operation of the human brain relies on functional networks enabled by inter-areal oscillatory synchronization between neuronal populations. Although disruptions in this functional connectivity are associated with brain disorders, evidence on its healthy age-dependent variation and behavioral relevance remains limited. Utilizing magnetoencephalography (MEG) recordings from 576 adults aged 18-87 years, we investigated the evolution of resting-state functional connectivity (rs-FC) across the healthy adult lifespan. We observed age-related, frequency-specific changes in widespread cortical networks. Alpha-band (8-13 Hz) rs-FC decreased, while delta (1-4 Hz), theta (4-8 Hz), and gamma-band (40-90 Hz) rs-FC increased with age. Beta-band (13-30 Hz) rs-FC followed a non-linear trajectory, peaking in middle age. The global delta, theta, alpha, and beta-band patterns differed from concurrent changes in oscillatory power, underscoring their dissociable contributions. Notably, reduced beta-band rs-FC was associated with increased sensorimotor attenuation, indicating that changes in rs-FC are behaviorally relevant for sensorimotor function. These findings advance our understanding of healthy brain aging and highlight a link between resting-state brain activity and sensorimotor integration. Key pointsO_LIFunctional connectivity is altered across the healthy adult lifespan in a frequency-dependent manner C_LIO_LIChanges in source power do not explain global functional connectivity trajectories C_LIO_LIBeta-band connectivity at rest is associated with sensorimotor attenuation independent of age-related effects C_LI

Published in Human Brain Mapping (predicted rank #3) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.