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

详细信息    查看全文

• 作者：Alex ; re Gramfort ; Theodore Papadopoulo ; Sylvain Baillet ; Maureen Clerc
• 关键词：Functional brain imaging ; Tracking ; Graph cut optimization ; Magnetoencephalography (MEG) ; Electroencephalography (EEG)
• 刊名：NeuroImage
• 出版年：2011
• 出版时间：1 February 2011
• 年：2011
• 卷：54
• 期：3
• 页码：1930-1941
• 全文大小：2502 K

文摘

This work proposes to use magnetoencephalography (MEG) and electroencephalography (EEG) source imaging to provide cinematic representations of the temporal dynamics of cortical activations. Cortical activation maps, seen as images of the active brain, are scalar maps defined 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 efficient graph cut 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 activation 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.