Paper published: A baseline for the multivariate comparison of resting state networks

In this paper we’ve put together a cohort of so-called “healthy normal controls” in the largest single group independent component analysis (ICA) forward model. Included in our analysis approach is a statistical methodology for performing mancova when the number of fMRI brain voxels (roughly V=60K) to test for activation differences over demographic variables is much larger than the number of subjects (N=603).  This massive project involved researchers nearly spanning our Medical Image Analysis Laboratory (MIALab), but many other investigators at the Mind Research Network (MRN).  It is a great example of what makes collaboration fun, challenging, and productive (thank you Elena, Eswar, Bill, Judith, Martin, and Srinivas).

A baseline for the multivariate comparison of resting state networks

Citation:Allen EA, Erhardt EB, Damaraju E, Gruner W, Segall JM, Silva RF, Havlicek M, Rachakonda S, Fries J, Kalyanam R, Michael AM, Caprihan A, Turner JA, Eichele T, Adelsheim S, Bryan AD, Bustillo J, Clark VP, Feldstein Ewing SW, Filbey F, Ford CC, Hutchison K, Jung RE, Kiehl KA, Kodituwakku P, Komesu YM, Mayer AR, Pearlson GD, Phillips JP, Sadek JR, Stevens M, Teuscher U, Thoma RJ and Calhoun VD (2011). A baseline for the multivariate comparison of resting state networks. Front. Syst. Neurosci. 5:2. doi: 10.3389/fnsys.2011.00002

Received: 16 Jun 2010; Accepted: 03 Jan 2011; Published online: 04 Feb 2011.


As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting state networks of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12 to 71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. Resting state networks were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

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