Inferring task-related networks using independent component analysis in magnetoencephalography.

NeuroImage
Henry LuckhooMark W Woolrich

Abstract

A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8-20 Hz frequency range, temporally down-sampled with windows of 1-4s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when ...Continue Reading

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Citations

Mar 29, 2014·ELife·Adam P BakerMark Woolrich
Sep 6, 2014·BMC Neuroscience·June Sic KimChun Kee Chung
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Software Mentioned

PsychoPy
fastICA
ICASSO

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