Sparse regression models of pain perception

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

*Corresponding author for this work

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

8 Scopus citations


Discovering brain mechanisms underlying pain perception remains a challenging neuroscientific problem with important practical applications, such as developing better treatments for chronic pain. Herein, we focus on statistical analysis of functional MRI (fMRI) data associated with pain stimuli. While the traditional mass-univariate GLM [8] analysis of pain-related brain activation can miss potentially informative voxel interaction patterns, our approach relies instead on multivariate predictive modeling methods such as sparse regression (LASSO [17] and, more generally, Elastic Net (EN) ([18]) that can learn accurate predictive models of pain and simultaneously discover brain activity patterns (relatively small subsets of voxels) allowing for such predictions. Moreover, we investigate the effect of temporal (time-lagged) information, often ignored in traditional fMRI studies, on the predictive accuracy and on the selection of brain areas relevant to pain perception. We demonstrate that (1) Elastic Net regression can be highly predictive of pain perception, by far outperforming ordinary least-squares (OLS) linear regression; (2) temporal information is very important for pain perception modeling and can significantly increase the prediction accuracy; (3) moreover, regression models that incorporate temporal information discover brain activation patterns undetected by non-temporal models.

Original languageEnglish (US)
Title of host publicationBrain Informatics - International Conference, BI 2010, Proceedings
Number of pages12
StatePublished - 2010
Event2010 International Conference on Brain Informatics, BI 2010 - Toronto, ON, Canada
Duration: Aug 28 2010Aug 30 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6334 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2010 International Conference on Brain Informatics, BI 2010
CityToronto, ON

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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