Tracking cortical activity from M/EEG using graph-cuts with spatiotemporal constraints.

Abstract : This work proposes to use magnetoencephalography (MEG) and electroencephalography (EEG) source imaging to provide cinematic representations of the temporal dynamics of cortical activations. Cortical activations maps, seen as images of the active brain, are scalar maps de_ned at the vertices of a triangulated cortical surface. They can be computed from M/EEG data using a linear inverse solver every millisecond. Taking as input these activation maps and exploiting both the graph structure of the cortical mesh and the high sampling rate of M/EEG recordings, neural activations are tracked over time using an e_cient graph-cuts based algorithm. The method estimates the spatiotemporal support of the active brain regions. It consists in computing a minimum cut on a particularly designed weighted graph imposing spatiotemporal regularity constraints on the activations patterns. Each node of the graph is assigned a label (active or non-active). The method works globally on the full time-period of interest, can cope with spatially extended active regions and allows the active domain to exhibit topology changes over time. The algorithm is illustrated and validated on synthetic data. Results of the method are provided on two MEG cognitive experiments in the visual and somatosensory cortices, demonstrating the ability of the algorithm to handle various types of data.
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Journal articles
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https://hal.inria.fr/inria-00526020
Contributor : Alexandre Gramfort <>
Submitted on : Wednesday, October 13, 2010 - 2:30:03 PM
Last modification on : Thursday, March 7, 2019 - 3:34:14 PM

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Alexandre Gramfort, Théodore Papadopoulo, Sylvain Baillet, Maureen Clerc. Tracking cortical activity from M/EEG using graph-cuts with spatiotemporal constraints.. NeuroImage, Elsevier, 2010, epub ahead of print. ⟨10.1016/j.neuroimage.2010.09.087⟩. ⟨inria-00526020⟩

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