Analyses of regional-average activation and multivoxel pattern information tell complementary stories

Koji Jimura, Russell A. Poldrack

Research output: Contribution to journalArticlepeer-review

140 Citations (Scopus)


Multivariate pattern analysis (MVPA) has recently received increasing attention in functional neuroimaging due to its ability to decode mental states from fMRI signals. However, questions remain regarding both the empirical and conceptual relationships between results from MVPA and standard univariate analyses. In the current study, whole-brain univariate and searchlight MVPAs of parametric manipulations of monetary gain and loss in a decision making task (Tom et al., 2007) were compared to identify the differences in the results across these methods and the implications for understanding the underlying mental processes. The MVPA and univariate results did identify some overlapping regions in whole brain analyses. However, an analysis of consistency revealed that in many regions the effect size estimates obtained from MVPA and univariate analysis were uncorrelated. Moreover, comparison of sensitivity showed a general trend towards greater sensitivity to task manipulations by MVPA compared to univariate analysis. These results demonstrate that MVPA methods may provide a different view of the functional organization of mental processing compared to univariate analysis, wherein MVPA is more sensitive to distributed coding of information whereas univariate analysis is more sensitive to global engagement in ongoing tasks. The results also highlight the need for better ways to integrate these methods.

Original languageEnglish
Pages (from-to)544-552
Number of pages9
Issue number4
Publication statusPublished - 2012 Mar
Externally publishedYes


  • Decision-making
  • FMRI
  • MVPA
  • Support vector regression
  • Univariate analysis

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Behavioral Neuroscience


Dive into the research topics of 'Analyses of regional-average activation and multivoxel pattern information tell complementary stories'. Together they form a unique fingerprint.

Cite this