Characterization of groups using composite kernels and multi-source fMRI analysis data: Application to schizophrenia

Eduardo Castro, Manel Martínez-Ramón, Godfrey Pearlson, Jing Sui, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA.

Original languageEnglish (US)
Pages (from-to)526-536
Number of pages11
Issue number2
StatePublished - Sep 15 2011
Externally publishedYes


  • Composite kernels
  • FMRI
  • Feature selection
  • Independent component analysis
  • Pattern classification
  • Recursive feature elimination
  • Schizophrenia
  • Support vector machines

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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