A Baseline for the Multivariate Comparison of Resting-State Networks

TLDR

Large functional and structural MRI datasets require a robust baseline, efficient preprocessing, and reproducible statistical methods to determine diagnostic relevance. The study introduces a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. The approach was applied to 603 healthy participants, with data collected on a single scanner, preprocessed via an automated SPM pipeline, and analyzed using group ICA to assess resting‑state networks across time‑course spectral power, spatial map intensity, and functional connectivity. Age produced robust decreases in network coherence and connectivity across all outcome measures, while gender differences were smaller, with females showing stronger intra‑network connectivity and males showing greater inter‑network connectivity, particularly in sensorimotor networks, establishing a baseline for future brain‑network studies.

Abstract

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.