Elsevier

NeuroImage

Volume 92, 15 May 2014, Pages 217-224
NeuroImage

Intersubject consistency of cortical MEG signals during movie viewing

https://doi.org/10.1016/j.neuroimage.2014.02.004Get rights and content
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Highlights

  • Intersubject synchronization during movie viewing was studied with MEG.

  • Correlations were computed by multi-set canonical correlation analysis (M-CCA).

  • Time courses correlated between subjects at < 10 Hz and at the frame rate.

Abstract

According to recent functional magnetic resonance imaging (fMRI) studies, spectators of a movie may share similar spatiotemporal patterns of brain activity. We aimed to extend these findings of intersubject correlation to temporally accurate single-trial magnetoencephalography (MEG). A silent 15-min black-and-white movie was shown to eight subjects twice. We adopted a spatial filtering model and estimated its parameter values by using multi-set canonical correlation analysis (M-CCA) so that the intersubject correlation was maximized. The procedure resulted in multiple (mutually uncorrelated) time-courses with statistically significant intersubject correlations at frequencies below 10 Hz; the maximum correlation was 0.28 ± 0.075 in the ≤ 1 Hz band. Moreover, the 24-Hz frame rate elicited steady-state responses with statistically significant intersubject correlations up to 0.29 ± 0.12. To assess the brain origin of the across-subjects correlated signals, the time-courses were correlated with minimum-norm source current estimates (MNEs) projected to the cortex. The time series implied across-subjects synchronous activity in the early visual, posterior and inferior parietal, lateral temporo-occipital, and motor cortices, and in the superior temporal sulcus (STS) bilaterally. These findings demonstrate the capability of the proposed methodology to uncover cortical MEG signatures from single-trial signals that are consistent across spectators of a movie.

Keywords

Magnetoencephalography (MEG)
Minimum norm estimate (MNE)
Movie
Multi-set canonical correlation analysis (M-CCA)
Spatial filtering

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