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Neuroimage. 2010 Feb 1;49(3):2163-77. doi: 10.1016/j.neuroimage.2009.10.080. Epub 2009 Nov 5.

Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach.

Author information

  • 1Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY, USA. xinian.zuo@nyumc.org

Abstract

Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional networks in the brain, referred to as intrinsic connectivity networks (ICNs). Despite a burgeoning literature, researchers continue to struggle with the challenge of defining computationally efficient and reliable approaches for identifying and characterizing ICNs. Independent component analysis (ICA) has emerged as a powerful tool for exploring ICNs in both healthy and clinical populations. In particular, temporal concatenation group ICA (TC-GICA) coupled with a back-reconstruction step produces participant-level resting state functional connectivity maps for each group-level component. The present work systematically evaluated the test-retest reliability of TC-GICA derived RSFC measures over the short-term (<45 min) and long-term (5-16 months). Additionally, to investigate the degree to which the components revealed by TC-GICA are detectable via single-session ICA, we investigated the reproducibility of TC-GICA findings. First, we found moderate-to-high short- and long-term test-retest reliability for ICNs derived by combining TC-GICA and dual regression. Exceptions to this finding were limited to physiological- and imaging-related artifacts. Second, our reproducibility analyses revealed notable limitations for template matching procedures to accurately detect TC-GICA based components at the individual scan level. Third, we found that TC-GICA component's reliability and reproducibility ranks are highly consistent. In summary, TC-GICA combined with dual regression is an effective and reliable approach to exploratory analyses of resting state fMRI data.

Copyright (c) 2009 Elsevier Inc. All rights reserved.

PMID:
19896537
[PubMed - indexed for MEDLINE]
PMCID:
PMC2877508
Free PMC Article

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