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A baseline for the multivariate comparison of resting-state networks.

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 (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12-71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs 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.

Pubmed ID: 21442040 RIS Download

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Associated grants

  • Agency: NIMH NIH HHS, Id: R01 MH072681
  • Agency: NIBIB NIH HHS, Id: R01 EB005846
  • Agency: NIBIB NIH HHS, Id: R01 EB006841
  • Agency: NIAAA NIH HHS, Id: P20 AA017068
  • Agency: NIBIB NIH HHS, Id: R01 EB000840
  • Agency: NCRR NIH HHS, Id: P20 RR021938
  • Agency: NINDS NIH HHS, Id: R21 NS064464
  • Agency: NIDA NIH HHS, Id: K01 DA021632
  • Agency: NIDA NIH HHS, Id: R03 DA022435
  • Agency: NIDA NIH HHS, Id: R03 DA024212

NeuroSynth (Data, Activation Foci)

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