A baseline for the multivariate comparison of resting state networks

Frontiers in Systems Neuroscience

Joseph R Sadek¹  Elena A Allen³  Arvind Caprihan³  Andrew MMichael³  Sarah W Feldstein Ewing³  Jill Fries³  Eswar Damaraju³  Srinivas Rachakonda³  Erik BErhardt³  Tom Eichele⁴  Angela D Bryan⁵  Rogers FSilva⁵  Jessica A Turner⁵  Rex E Jung⁵  John P Phillips⁵  Martin eHavlicek⁵  Vincent P Clark⁵  Robert J Thoma⁵  Kent A Kiehl⁵  Yuko M Komesu⁵  Vince D Calhoun⁵  Piyadasa eKodituwakku⁵  Steven eAdelsheim⁵  Judith M Segall⁵  William Gruner⁵  Juan eBustillo⁵  Ravi Kalyanam⁵  Kent eHutchison⁵  Corey C Ford⁵  Ursina eTeuscher⁵  Andrew R Mayer⁵  Francesca eFilbey⁶  Godfrey D Pearlson⁷  Michael Stevens⁷ 

[1] New Mexico VA Health Care System;Olin Neuropsychiatry Research Center;The Mind Research Network;University of Bergen;University of New Mexico;University of Texas at Dallas;Yale University;

关键词: fMRI;    functional connectivity;    Independent Component Analysis;    connectome;    resting-state;   

DOI  :  10.3389/fnsys.2011.00002

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown