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Proceedings Paper

Sparse regression analysis of task-relevant information distribution in the brain
Author(s): Irina Rish; Guillermo A. Cecchi; Kyle Heuton; Marwan N. Baliki; A. Vania Apkarian
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Paper Abstract

One of key topics in fMRI analysis is discovery of task-related brain areas. We focus on predictive accuracy as a better relevance measure than traditional univariate voxel activations that miss important multivariate voxel interactions. We use sparse regression (more specifically, the Elastic Net1) to learn predictive models simultaneously with selection of predictive voxel subsets, and to explore transition from task-relevant to task-irrelevant areas. Exploring the space of sparse solutions reveals a much wider spread of task-relevant information in the brain than it is typically suggested by univariate correlations. This happens for several tasks we considered, and is most noticeable in case of complex tasks such as pain rating; however, for certain simpler tasks, a clear separation between a small subset of relevant voxels and the rest of the brain is observed even with multivariate approach to measuring relevance.

Paper Details

Date Published: 14 February 2012
PDF: 8 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831412 (14 February 2012); doi: 10.1117/12.911318
Show Author Affiliations
Irina Rish, IBM Thomas J. Watson Research Ctr. (United States)
Guillermo A. Cecchi, IBM Thomas J. Watson Research Ctr. (United States)
Kyle Heuton, Univ. of Minnesota, Twin Cities (United States)
Marwan N. Baliki, Northwestern Univ. (United States)
A. Vania Apkarian, Northwestern Univ. (United States)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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