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

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 in (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. (C) 2009 Elsevier Inc All rights reserved