Role of white-matter pathways in coordinating alpha oscillations in resting visual cortex

Abstract

In the absence of cognitive tasks and external stimuli, strong rhythmic fluctuations with a frequency ≈ 10 Hz emerge from posterior regions of human neocortex. These posterior α-oscillations can be recorded throughout the visual cortex and are particularly strong in the calcarine sulcus, where the primary visual cortex is located. The mechanisms and anatomical pathways through which local \alpha-oscillations are coordinated however, are not fully understood. In this study, we used a combination of magnetoencephalography (MEG), diffusion tensor imaging (DTI), and biophysical modeling to assess the role of white-matter pathways in coordinating cortical α-oscillations. Our findings suggest that primary visual cortex plays a special role in coordinating α-oscillations in higher-order visual regions. Specifically, the amplitudes of α-sources throughout visual cortex could be explained by propagation of α-oscillations from primary visual cortex through white-matter pathways. In particular, α-amplitudes within visual cortex correlated with both the anatomical and functional connection strengths to primary visual cortex. These findings reinforce the notion of posterior α-oscillations as intrinsic oscillations of the visual system. We speculate that they might reflect a default-mode of the visual system during which higher-order visual regions are rhythmically primed for expected visual stimuli by α-oscillations in primary visual cortex.

Structure‐function relationships in the visual system in multiple sclerosis: an MEG and OCT study

“…The outer retinal layer thickness in the latter group was positively related to alpha band connectivity. A potential explanation may again be that optic neuritis causes damage to white matter tracts, which are especially known to be mediating alpha band oscillations 29. For the MSNON group, we found that delta band connectivity was related to thickness of the outer retinal layers.…”

Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

“…1). We used the widely-used and freely available AAL template (Tzourio-Mazoyer et al, 2002) to parcellate the brain into 90 non-cerebellar regions in line with previous studies of resting-state MEG data (Cabral et al, 2014b; Hindriks et al, 2015; Brookes et al, 2016). Note however that, although this anatomy-derived parcellation serves for the purpose of the current work, it is unlikely to perform as well as a function-derived parcellation involving PCA (Baker et al, 2014; Colclough et al, 2015) .…”

Single or Multi-Frequency generators in on-going brain activity: a mechanistic whole-brain model of empirical MEG data

“…A scalar implementation of the LCMV beamformer was applied to estimate the source level activity of the MEG sensor data at each brain area (Van Veen et al, 1997, Sekihara et al, 2001, Woolrich et al, 2011. We used the widely-used and freely available AAL template (Tzourio-Mazoyer et al, 2002) to parcellate the brain into 90 non-cerebellar regions in line with previous studies of resting-state MEG data (Cabral et al, 2014b, Hindriks et al, 2015, Brookes et al, 2016. Note however that, although this anatomy-derived parcellation serves for the purpose of the current work, it is unlikely to perform as well as a function-derived parcellation involving PCA (Baker et al, 2014, Colclough et al, 2015.…”