Dynamic recruitment of resting state sub-networks

• DOI: 10.1016/j.neuroimage.2015.04.030
• Publication Date: 2015-04-18T17:46:21Z
• Authors: George C. O'Neill; Markus Bauer; Mark W. Woolrich; Peter G. Morris; Gareth R. Barnes; Matthew J. Brookes
• Keywords: Magnetoencephalography; Dynamic functional connectivity; Human Connectome Project; Connectomics; Temporal resolution; Network Dynamics

Resting state networks (RSNs) are of fundamental importance in human systems neuroscience with evidence suggesting that they integral to healthy brain function and perturbed pathology. Despite rapid progress this area, the temporal dynamics governing functional connectivities underlie RSN structure remain poorly understood. Here, we present a framework help further our understanding dynamics. We describe methodology which exploits direct nature high resolution magnetoencephalography (MEG). This technique, builds on previous work, extends from solving confounds MEG (source leakage) multivariate modelling transient connectivity. The resulting processing pipeline facilitates (electrophysiological) measurement dynamic networks. Our results show that, when connectivity is assessed small time windows, canonical sensorimotor network can be decomposed into number transiently synchronising sub-networks, recruitment depends current mental state. These rapidly changing sub-networks spatially focal with, for example, bilateral primary sensory motor areas resolved two separate sub-networks. likely interpretation larger most often seen neuroimaging studies reflects only aggregate these approach opens new frontiers study dynamics, showing capable revealing spatial, spectral signature connectome health disease.