Temporal dynamics of spontaneous MEG activity in brain networks

Abstract

Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of band-limited power primarily within the theta, alpha, and beta bands-that is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses.

Fig. 1.

Dorsal attention network. (Left) fMRI connectivity. A group conjunction map was obtained as in ref. by combining voxel-wise individual temporal correlation maps of BOLD signal time series obtained from the four main nodes of the DAN (coordinates listed in Table S1). The map shows voxels with significant temporal correlation in at least three of four seeds. The represented quantity is seed count. (Center) Stationary MEG connectivity. Group average temporal correlation map of wide-band (1-150 Hz) power time-courses obtained from averaging individual temporal correlation maps obtained from seeding LpIPS, one of the core nodes of the DAN. The represented quantity is Pearson's r, and only values above the average correlation in the whole brain (r = 0.7) are displayed. (Right) Nonstationary MEG connectivity using MCW algorithm (seed, LpIPS; external node, RSFG). The represented quantity is a t statistic comparing voxelwise correlation with the seed vs. the mean correlation of the seed with the rest of the brain. pIPS, posterior intraparietal sulcus; FEF, frontal eye field; vIPS, ventral intraparietal sulcus; MT, middle temporal.

Fig. 2.

Default mode network. (Left) fMRI conjunction maps obtained as in Fig. 1 corresponding to the DMN nodes. (Center) Stationary MEG connectivity obtained as in Fig. 1 (seed, LAG). Only values above the average correlation in the whole brain (r = 0.6) are reported. (Right) Nonstationary MEG connectivity using MCW algorithm (seed, LAG; external node, RSFG). L/R AG, angular gyrus; LPCC, left posterior cingulate cortex; L/R MPFC, medial prefrontal cortex; LRS, retrosplenial; L/R SFS, superior frontal sulcus; LHIP, left hippocampus.

Fig. 3.

(A) Wide-band MEG power time series (Eq. 2) for the main nodes in the DAN (LpIPS, RpIPS, LFEF, and RFEF). (B) Correlation time series pairing LpIPS with other nodes in the DAN and one region outside of the DAN (RSFG). The represented quantity is (Eq. 3; ) evaluated at time increments of 0.2 s. (C) Power spectral densities (PSD) of wide-band power (Eq. 2) in DAN and DMN nodes averaged across sessions and subjects. (D) Total interdependence measure (SI Text) for DAN and DMN on a semilog scale.

Fig. 4.

Band-specific nonstationary MEG connectivity. (Left) Dorsal attention network (DAN; seed, LpIPS; external node, RSFG). The represented quantity is the t statistic as in Figs. 1 and 2 and one horizontal slice containing the main DAN nodes is shown (z = 50, MNI152). (Right) Default mode network (DMN, seed, LAG; external node, RSFG). Two horizontal slices containing the principal DMN nodes are shown (z = 34 and z = 23). No MCWs were identified in the gamma band for the DMN, indicating that this band contributes less to the connectivity than the others. The central part of the brain has been masked due to the limited accuracy of inverse source localization in that region.