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A spatially constrained low-rank matrix factorization for the functional parcellation of the brain

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We propose a new matrix recovery framework to partition brain activity using time series of resting-state functional Magnetic Resonance Imaging (fMRI). Spatial clusters are obtained with a new low-rank factorization algorithm that offers the ability to add different types of constraints. As an example we add a total variation type cost function in order to exploit neighborhood constraints.
We first validate the performance of our algorithm on sim- ulated data, which allows us to show that the neighborhood constraint improves the recovery in noisy or undersampled set-ups. Then we conduct experiments on real-world data, where we simulated an accelerated acquisition by randomly undersampling the time series. The obtained parcellation are reproducible when analysing data from different sets of indi- viduals, and the estimation is robust to undersampling.

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Benichoux, A. and Blumensath, T. (2014) A spatially constrained low-rank matrix factorization for the functional parcellation of the brain Proc. 22nd European Signal Processing Conference, pp. 1-5.

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Submitted date: 1 September 2014
Published date: October 2014
Organisations: Signal Processing & Control Grp


Local EPrints ID: 363425
PURE UUID: c68323f6-4f6d-4fa1-9d9e-9f4ff9a14760

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Date deposited: 25 Mar 2014 11:50
Last modified: 18 Jul 2017 02:40

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Author: A. Benichoux
Author: T. Blumensath

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