Investigating the electrophysiological basis of resting state networks using magnetoencephalography

Abstract

In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenation-level-dependent (BOLD) signals from different brain areas. However, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.

Fig. 1.

Comparison of brain networks obtained using ICA independently on MEG and fMRI data. (A) DMN (α); (B) left lateral frontoparietal network (β); (C) right lateral frontoparietal network (β); (D) sensorimotor network (β); (E) medial parietal regions (β); (F) visual network (β); (G) frontal lobes including anterior cingulate cortex (β); (H) cerebellum (β). (A–H) Upper, fMRI (thresholded at Z = 3); Lower, MEG [thresholded at a correlation coefficient of 0.3, apart from the left lateralized frontoparietal network (B) in which the threshold was reduced to 0.16 for visualization].

Fig. 2.

MEG seed-based correlation analysis in the β-band. (A) Motor network: Top, fMRI (ICA result); Middle, MEG, right motor seed; Bottom, MEG, left motor seed. (B) FP network: Top, fMRI; Middle, MEG, right parietal seed; Bottom, MEG, left parietal seed. (C) Visual network: Top, fMRI; Middle, MEG, left visual seed; Bottom, MEG, right visual seed. (D and E) Correlation between the parietal and motor areas compared with correlation between parietal and frontal areas in the right (D) and left (E) hemispheres. (F) Comparison between correlation measured between left visual and left parietal, right visual and right parietal, and left/right parietal and left/right visual. In D–F, colored overlays represent the network nodes and are based on fMRI data.

Fig. 3.

MEG seed-based correlation analysis across frequencies. (A) The FP network. (A, i) Left lateral parietal [MNI (−48, −70, 20) mm] and prefrontal [MNI (−34, 20, 44) mm] cortices. (A, ii) Right lateral parietal [MNI (42, −70, 24) mm] and prefrontal [MNI (18, 20, 40) mm] cortices. (B) DMN. (B, i) Anterior cingulate [MNI (−4, 50, 14) mm] and right inferior parietal lobule [MNI (56, −54, 16) mm]. (B, ii) Anterior cingulate and left inferior parietal lobule [MNI (−56, −62, 16) mm]. (B, iii) Left and right inferior parietal lobules. (B, iv) Connectivity between right inferior parietal lobule and the right primary visual cortex.

Fig. 4.

(A) fMRI-derived temporal correlation matrix (%). (B) Comparison of temporal correlation between MEG and fMRI: Upper, correlation between the DMN tIC (α-band) and tICs for all other networks (β-band); Lower, equivalent temporal correlation derived using fMRI.