Identification of multimodal MRI and EEG biomarkers using joint-ICA and divergence criteria

V. Calhoun, R. Silva, J. Liu

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

Abstract

The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via a joint histogram visualization technique. Evaluation of which combination of fused data is most useful is determined by using several information theoretic divergence measures. We demonstrate our method on a data set composed of functional MRI data from two tasks, structural MRI data, and EEG data collected on patients with schizophrenia and healthy controls. Our method provides a way to improve feature selection and even preprocessing. We show that combining data types can improve our ability to distinguish differences between groups.

Original languageEnglish (US)
Title of host publicationMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
Pages151-156
Number of pages6
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
Event17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - Thessaloniki, Greece
Duration: Aug 27 2007Aug 29 2007

Publication series

NameMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP

Other

Other17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
CountryGreece
CityThessaloniki
Period8/27/078/29/07

ASJC Scopus subject areas

  • Computer Science(all)
  • Signal Processing

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