A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework

Jing Sui, Tülay Adali, Godfrey D. Pearlson, Vincent P. Clark, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Independent component analysis (ICA) is a promising method that is increasingly used to analyze brain imaging data such as functional magnetic resonance imaging (fMRI), structural MRI, and electroencephalography and has also proved useful for group comparison, e.g., differentiating healthy controls from patients. An advantage of ICA is its ability to identify components that are mixed in an unknown manner. However, ICA is not necessarily robust and optimal in identifying between-group effects, especially in highly noisy situations. Here, we propose a modified ICA framework for multigroup data analysis that incorporates prior information regarding group membership as a constraint into the mixing coefficients. Our approach, called coefficient-constrained ICA (CC-ICA), prioritizes identification of components that show a significant group difference. The performance of CC-ICA via synthetic and hybrid data simulations is evaluated under different hypothesis testing assumptions and signal to noise ratios (SNRs). Group analysis is also conducted on real multitask fMRI data. Results show that CC-ICA improves the estimation accuracy of the independent components greatly, especially those that have different patterns for different groups (e.g., patients vs. controls); In addition, it enhances the data extraction sensitivity to group differences by ranking components with P value or J-divergence more consistently with the ground truth. The proposed algorithm performs quite well for both group-difference detection and multitask fMRI data fusion, which may prove especially important for the identification of relevant disease biomarkers.

Original languageEnglish (US)
Pages (from-to)2953-2970
Number of pages18
JournalHuman Brain Mapping
Issue number9
StatePublished - Sep 15 2009
Externally publishedYes


  • Biomarker
  • CC-ICA
  • Group difference
  • Independent component analysis
  • Infomax
  • Mixing coefficients
  • fMRI

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology


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