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Proceedings Paper

Connectivity-based parcellation of the postcentral gyrus using a spectral approach
Author(s): Tristan Moreau; Bernard Gibaud
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Paper Abstract

Subdividing the cortex into structural elements, known as parcellation, is a key aspect to apprehend the link between structure and function in the brain. A very exciting idea to parcellate the cortex and thus to construct the human connectome is to suppose that all structural elements of the cortex share similar connectivity patterns : this process defines a connectivity-based parcellation. We address the problem of the connectivity-based parcellation without anatomical priors using the highly efficient normalized cut algorithm to classify, in a reproducible way, a large data set of connectivity patterns. The idea was to model the seeds topology as a graph in which each node represents a seed, the edges between two nodes represent the local neighbourhood relationships of the seed, and weights of the edges represent the similarity of the two connectional fingerprints of the corresponding seeds. This connectivity-based parcellation was applied both on a phantom and on the left postcentral gyrus of four different subjects. For the real data set, the structural connectivity pattern of each seed, located on the surface of the grey/white interface of the left postcentral gyrus, was reconstructed from diffusion magnetic resonance imaging data. These connectivity patterns were characterised using a probabilistic tractography based on a model of diffusion which could take into account up to two fibers in each voxel. Finally, the left postcentral gyrus of each subject was parcellated in twelve parcels.

Paper Details

Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692B (13 March 2013); doi: 10.1117/12.2006875
Show Author Affiliations
Tristan Moreau, MediCIS team, INSERM (France)
Bernard Gibaud, MediCIS team, INSERM (France)


Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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