Abstract
Functional connectivity measures applied to magnetoencephalography (MEG) data have the capacity to elucidate neuronal networks. However, the task-related modulation of these measures is essential to identifying the functional relevance of the identified network. In this study, we provide evidence for the efficacy of measuring “state-related” (i.e., task vs. rest) changes in MEG functional connectivity for revealing a sensorimotor network. We investigate changes in functional connectivity, measured as cortico-cortical coherence (CCC), between rest blocks and the performance of a visually directed motor task in a healthy cohort. Task-positive changes in CCC were interpreted in the context of any concomitant modulations in spectral power. Task-related increases in whole-head CCC relative to the resting state were identified between areas established as part of the sensorimotor network as well as frontal eye fields and prefrontal cortices, predominantly in the beta and gamma frequency bands. This study provides evidence for the use of MEG to identify task-specific functionally connected sensorimotor networks in a non-invasive, patient friendly manner.
Highlights
Coordinated activity of brain regions is essential for integrating multiple information streams into a task-specific strategy for response
The first latent variables (LVs) reveals that Functional connectivity (FC) is modulated between performance of the motor task and rest in this subject group
A task-related increase in FC occurred between brain areas involved in movement and somatosensation (MI/SI), motor planning (PMC/supplementary motor area (SMA)/CB), Figure 2
Summary
Coordinated activity of brain regions is essential for integrating multiple information streams into a task-specific strategy for response. Further work is necessary to elucidate the mechanisms that coordinate and control functional and pathological neural networks. Functional connectivity (FC) analysis of noninvasive neuroimaging is essential for extending our knowledge about how neural networks are dynamically modulated. The most common non-invasive neuroimaging techniques for FC analysis are functional magnetic resonance imaging (fMRI), electro- and magneto-encephalography (EEG/MEG). MEG has several advantages for FC analysis over EEG and fMRI. In comparison to fMRI, MEG records a direct correlate of neural activity with high temporal resolution, while the blood oxygen level dependent (BOLD) response is a slower, indirect measure of neural activity [6,7]. A recent study of interest has shown reasonable within-subject correspondence in networks identified using FC measures derived from MEG and fMRI data [8].
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