Group learning using contrast NMF: Application to functional and structural MRI of schizophrenia

Vamsi K. Potluru, Vince D. Calhoun

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

Abstract

Non-negative Matrix factorization (NMF) has increasingly been used as a tool in signal processing in the last couple of years. NMF, like independent component analysis (ICA) is useful for decomposing high dimensional data sets into a lower dimensional space. Here, we use NMF to learn the features of both structural and functional magnetic resonance imaging (sMRI/fMRI) data. NMF can be applied to perform group analysis of imaging data and we apply it to learn the spatial patterns which linearly covary among subjects for both sMRI and fMRI. We add an additional contrast term to NMF (called co-NMF) to identify features distinctive between two groups. We apply our approach to a dataset consisting of schizophrenia patients and healthy controls. The results from co-NMF make sense in light of expectations and are improved compared to the NMF results. Our method is general and may prove to be a useful tool for identifying differences between multiple groups.

Original languageEnglish (US)
Title of host publication2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008
Pages1336-1339
Number of pages4
DOIs
StatePublished - Sep 19 2008
Externally publishedYes
Event2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008 - Seattle, WA, United States
Duration: May 18 2008May 21 2008

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Other

Other2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008
Country/TerritoryUnited States
CitySeattle, WA
Period5/18/085/21/08

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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