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Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis

Authors

  • Pavan Ramkumar,

    Corresponding author
    1. Low Temperature Laboratory, Brain Research Unit, Aalto University School of Science, Aalto, Finland
    • Brain Research Unit, Low Temperature Laboratory, Aalto University School of Science, PO Box 15100, FI-00076 Aalto, Finland
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  • Lauri Parkkonen,

    1. Low Temperature Laboratory, Brain Research Unit, Aalto University School of Science, Aalto, Finland
    2. Advanced Magnetic Imaging Centre, Aalto University School of Science, Finland
    3. Cognitive Neuroimaging Unit, INSERM-CEA/NeuroSpin, Gif-sur-Yvette, France
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  • Riitta Hari,

    1. Low Temperature Laboratory, Brain Research Unit, Aalto University School of Science, Aalto, Finland
    2. Advanced Magnetic Imaging Centre, Aalto University School of Science, Finland
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  • Aapo Hyvärinen

    1. Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
    2. Department of Computer Science, Helsinki Institute of Information Technology, University of Helsinki, Helsinki, Finland
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Abstract

Independent component analysis (ICA) of electroencephalographic (EEG) and magnetoencephalographic (MEG) data is usually performed over the temporal dimension: each channel is one row of the data matrix, and a linear transformation maximizing the independence of component time courses is sought. In functional magnetic resonance imaging (fMRI), by contrast, most studies use spatial ICA: each time point constitutes a row of the data matrix, and independence of the spatial patterns is maximized. Here, we show the utility of spatial ICA in characterizing oscillatory neuromagnetic signals. We project the sensor data into cortical space using a standard minimum-norm estimate and apply a sparsifying transform to focus on oscillatory signals. The resulting method, spatial Fourier-ICA, provides a concise summary of the spatiotemporal and spectral content of spontaneous neuromagnetic oscillations in cortical source space over time scales of minutes. Spatial Fourier-ICA applied to resting-state and naturalistic stimulation MEG data from nine healthy subjects revealed consistent components covering the early visual, somatosensory and motor cortices with spectral peaks at ∼10 and ∼20 Hz. The proposed method seems valuable for inferring functional connectivity, stimulus-related modulation of rhythmic activity, and their commonalities across subjects from nonaveraged MEG data. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.

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