Exploration of the optimal group-discriminating features using CC-ICA

Jing Sui, Vince D. Calhoun

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

Abstract

A coefficient-constrained independent component analysis (CC-ICA) framework for second-level group analysis is proposed, which incorporates group membership information as a constraint into the mixing coefficients. Applications to simulated signals and hybrid fMRI data show that, compared with regular ICA, CC-ICA improves both the decomposition accuracy and the extraction sensitivity to group differences. CCICA is then applied to real fMRI data to explore the optimal tasks and features from 15 task combinations. Results are consistent with and extend various neuroimaging studies and may prove especially important for the identification of relevant biomarkers of brain disorders.

Original languageEnglish (US)
Title of host publication2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008
Pages1410-1414
Number of pages5
DOIs
StatePublished - Dec 1 2008
Externally publishedYes
Event2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008 - Pacific Grove, CA, United States
Duration: Oct 26 2008Oct 29 2008

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008
CountryUnited States
CityPacific Grove, CA
Period10/26/0810/29/08

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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