Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data

Pavan Ramkumar, Lauri Parkkonen, Aapo Hyvärinen
2014 NeuroImage  
Highlights: (1) We propose a method to extract narrowband, long-range envelope correlations at the cortical level. (2) We apply the method to resting-state MEG data to characterize spontaneous oscillatory networks. (3) We interpret these networks by studying their dynamics during naturalistic stimulation. Abstract We developed a data-driven method to spatiotemporally and spectrally characterize the dynamics of brain oscillations in resting-state magnetoencephalography (MEG) data. The method,
more » ... led envelope spatial Fourier independent component analysis (eSFICA), maximizes the spatial and spectral sparseness of Fourier energies of a cortically constrained source current estimate. We compared this method using a simulated data set against 5 other variants of independent component analysis and found that eSFICA performed best in characterizing dynamics at time scales of the order of minutes. We then applied eSFICA to real MEG data obtained from 9 subjects during rest. The method identified several networks showing within-and cross-frequency long-range functional connectivity profiles which resemble previously reported resting-state networks, such as the bilateral sensorimotor network at ~20 Hz, the lateral and medial parieto-occipital sources at ~10 Hz , a subset of the default-mode network at ~8 and ~15 Hz, and lateralized temporal lobe sources at ~8 Hz. Finally, we interpreted the estimated networks as spatiospectral filters and applied the filters to obtain the dynamics during a naturalistic stimulus sequence presented to the same 9 subjects. We observed occipital alpha modulation to visual stimuli, bilateral rolandic mu modulation to tactile stimuli and video clips of hands, and the temporal lobe network modulation to speech stimuli, but no modulation of the sources in the default-mode network. We conclude that (1) the proposed method robustly detects long-range cross-frequency networks at long time scales, (2) the functional relevance of the resting-state networks can be probed by applying the obtained spatiospectral filters to data from measurements with controlled external stimulation.
doi:10.1016/j.neuroimage.2013.10.032 pmid:24185028 fatcat:rmwxjskq5rgnbj6rkqdrngs56m