Spatial ICA reveals functional activity hidden from traditional fMRI GLM-based analyses

Jiansong Xu, Marc N. Potenza, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

Abstract

Independent component analysis (ICA) is a signal processing technique using higher-order statistics to extract signals by unmixing signal mixtures. McKeown et al. (1998) introduced spatial ICA (sICA) into functional magnetic resonance imaging (fMRI) study in the late 1990s. SICA assumes that fMRI signal from each voxel represents a linear mixture of source signals, separates this signal mixture into spatially independent source signals using higher-order statistics, and groups all brain regions showing synchronized source signals into independent components (ICs), which represent temporally coherent functional networks (FNs) (McKeown and Sejnowski, 1998; McKeown et al., 1998; Calhoun et al., 2002, 2009). McKeown et al. (1998) predicted that sICA would be more sensitive in detecting task-related changes in fMRI signal than the traditional general linear model (GLM) based analysis, because sICA uses a data-driven approach, and can reduce noise in the final solution by separating artifacts from real fMRI signal.

Original languageEnglish (US)
Article number154
JournalFrontiers in Neuroscience
Issue number7 AUG
DOIs
StatePublished - Jan 1 2013
Externally publishedYes

Keywords

  • Functional connectivity
  • Functional magnetic resonance imaging (fMRI)
  • Functional network of the brain
  • General linear model (GLM)
  • Independent component analysis (ICA)

ASJC Scopus subject areas

  • Neuroscience(all)

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