Resolving the connectome – Spectrally-specific functional connectivity networks and their distinct contributions to behaviour

Abstract

The resting human brain exhibits spontaneous patterns of activity that reflect features of the underlying neural substrate. Examination of inter-areal coupling of resting state oscillatory activity has revealed that the brain’s resting activity is composed of functional networks, whose topographies differ depending upon oscillatory frequency, suggesting a role for carrier frequency as a means of creating multiplexed, or functionally segregated, communication channels between brain areas. Using canonical correlation analysis, we examined spectrally resolved resting-state connectivity patterns derived from magnetoencephalography (MEG) recordings to determine the relationship between connectivity intrinsic to different frequency channels and a battery of over a hundred behavioural and demographic indicators, in a group of 89 young healthy participants. We demonstrate that each of the classical frequency bands in the range 1-40Hz (delta, theta, alpha, beta and gamma) delineates a subnetwork that is behaviourally relevant, spatially distinct, and whose expression is either negatively or positively predictive of individual traits, with the strongest link in the alpha band being negative and networks oscillating at different frequencies, such as theta, beta and gamma carrying positive function.

Significance statement Even at rest, the human brain displays spontaneous coordinated rhythmic patterns of activity. Partitioning these according to their temporal properties reveals networks of distributed brain areas synchronized at different frequencies. The properties of these networks differ across individuals and are predictive of the brain’s response to tasks, pointing to a functional substrate underlying variability of task-related responses. The functional roles of these resting-state networks are yet to be fully elucidated. Here, in the absence of any task, we exploit the spectral richness of non-invasive magneto-encephalographic recordings to establish that individual differences in the expression of five spatio-spectrally distinct resting-state networks predict a diverse array of individual behavioural measures, from tobacco consumption to cognitive performance.