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Proceedings Paper

A regularized clustering approach to brain parcellation from functional MRI data
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Paper Abstract

We consider a data-driven approach for the subdivision of an individual subject's functional Magnetic Resonance Imaging (fMRI) scan into regions of interest, i.e., brain parcellation. The approach is based on a computational technique for calculating resolution from inverse problem theory, which we apply to neighborhood selection for brain connectivity networks. This can be efficiently calculated even for very large images, and explicitly incorporates regularization in the form of spatial smoothing and a noise cutoff. We demonstrate the reproducibility of the method on multiple scans of the same subjects, as well as the variations between subjects.

Paper Details

Date Published: 24 August 2017
PDF: 10 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940E (24 August 2017); doi: 10.1117/12.2274846
Show Author Affiliations
Keith Dillon, Tulane Univ. (United States)
Yu-Ping Wang, Tulane Univ. (United States)


Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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