Multivariate fMRI analysis using optimally-discriminative voxel-based analysis

Tianhao Zhang, Theodore D. Satterthwaite, Mark Elliott, Ruben C. Gur, Raquel E. Gur, Christos Davatzikos

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

Abstract

This significantly extends Multi-Voxel Pattern Analysis (MVPA) methods, such as the Searchlight and related methods, by building on an approach that was recently proposed for structural brain images, and was named Optimally-Discriminative Voxel-Based Analysis (ODVBA), which uses machine learning models to determine the optimal anisotropic filtering of images that enhances group differences. Precise spatial maps of activation are computed by tallying the weights of each voxel to all of the neighborhood in which it belongs, and significance maps are obtained via permutation testing. We adapt this idea to both single and multi-subject fMRI analysis. Both simulated data and real data from 12 adolescent subjects who completed a standard working memory task demonstrated the use of ODVBA in fMRI improves accuracy and spatial specificity of activation detection over Searchlight.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 2nd International Workshop on Pattern Recognition in NeuroImaging, PRNI 2012
Pages33-36
Number of pages4
DOIs
StatePublished - 2012
Event2012 2nd International Workshop on Pattern Recognition in NeuroImaging, PRNI 2012 - London, United Kingdom
Duration: Jul 2 2012Jul 4 2012

Publication series

NameProceedings - 2012 2nd International Workshop on Pattern Recognition in NeuroImaging, PRNI 2012

Other

Other2012 2nd International Workshop on Pattern Recognition in NeuroImaging, PRNI 2012
CountryUnited Kingdom
CityLondon
Period7/2/127/4/12

Keywords

  • MVPA
  • ODVBA
  • Searchlight
  • fMRI

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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