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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


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 Net 1) 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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2012
Subtitle of host publicationImage Processing
StatePublished - 2012
EventMedical Imaging 2012: Image Processing - San Diego, CA, United States
Duration: Feb 6 2012Feb 9 2012

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2012: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA


  • Elastic Net
  • Multivariate pattern analysis
  • Pain perception
  • Sparse regression
  • fMRI

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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