Frontiers in Systems Neuroscience (Feb 2011)

A baseline for the multivariate comparison of resting state networks

  • Elena A Allen,
  • Erik B Erhardt,
  • Eswar Damaraju,
  • William Gruner,
  • William Gruner,
  • Judith M Segall,
  • Judith M Segall,
  • Rogers F Silva,
  • Rogers F Silva,
  • Martin eHavlicek,
  • Martin eHavlicek,
  • Srinivas Rachakonda,
  • Jill Fries,
  • Ravi Kalyanam,
  • Ravi Kalyanam,
  • Andrew M Michael,
  • Arvind Caprihan,
  • Jessica A Turner,
  • Jessica A Turner,
  • Tom Eichele,
  • Steven eAdelsheim,
  • Angela D Bryan,
  • Angela D Bryan,
  • Angela D Bryan,
  • Juan eBustillo,
  • Juan eBustillo,
  • Vincent P Clark,
  • Vincent P Clark,
  • Vincent P Clark,
  • Sarah W Feldstein Ewing,
  • Francesca eFilbey,
  • Francesca eFilbey,
  • Corey C Ford,
  • Kent eHutchison,
  • Kent eHutchison,
  • Kent eHutchison,
  • Rex E Jung,
  • Rex E Jung,
  • Kent A Kiehl,
  • Kent A Kiehl,
  • Kent A Kiehl,
  • Piyadasa eKodituwakku,
  • Yuko M Komesu,
  • Andrew R Mayer,
  • Andrew R Mayer,
  • Godfrey D Pearlson,
  • Godfrey D Pearlson,
  • Godfrey D Pearlson,
  • John P Phillips,
  • John P Phillips,
  • Joseph R Sadek,
  • Joseph R Sadek,
  • Michael Stevens,
  • Michael Stevens,
  • Ursina eTeuscher,
  • Ursina eTeuscher,
  • Robert J Thoma,
  • Robert J Thoma,
  • Robert J Thoma,
  • Vince D Calhoun,
  • Vince D Calhoun,
  • Vince D Calhoun,
  • Vince D Calhoun

DOI
https://doi.org/10.3389/fnsys.2011.00002
Journal volume & issue
Vol. 5

Abstract

Read online

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.

Keywords