Sparse regression analysis of task-relevant information distribution in the brain

Irina Rish; Guillermo A. Cecchi; Kyle Heuton; Marwan N. Baliki; A. Vania Apkarian

doi:10.1117/12.911318

14 February 2012 Sparse regression analysis of task-relevant information distribution in the brain

Irina Rish, Guillermo A. Cecchi, Kyle Heuton, Marwan N. Baliki, A. Vania Apkarian

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 831412 (2012) https://doi.org/10.1117/12.911318
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

Citation Download Citation

Irina Rish, Guillermo A. Cecchi, Kyle Heuton, Marwan N. Baliki, and A. Vania Apkarian "Sparse regression analysis of task-relevant information distribution in the brain", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831412 (14 February 2012); https://doi.org/10.1117/12.911318

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