A baseline for the multivariate comparison of resting-state networks

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

    Research output: Contribution to journalArticle

    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 (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 identifed 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 sensor motor networks. These fndings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

    Original languageEnglish (US)
    Article number2
    JournalFrontiers in Systems Neuroscience
    Issue numberFEBRUARY 2011
    DOIs
    StatePublished - Feb 4 2011

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    Keywords

    • Connectome
    • Functional connectivity
    • Independent component analysis
    • Resting-state
    • fMRI

    ASJC Scopus subject areas

    • Neuroscience (miscellaneous)
    • Developmental Neuroscience
    • Cognitive Neuroscience
    • Cellular and Molecular Neuroscience

    Cite this

    Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner, W., Segall, J. M., Silva, R. F., Havlicek, M., Rachakonda, S., Fries, J., Kalyanam, R., Michael, A. M., Caprihan, A., Turner, J. A., Eichele, T., Adelsheim, S., Bryan, A. D., Bustillo, J., Clark, V. P., Ewing, S. W. F., ... Calhoun, V. D. (2011). A baseline for the multivariate comparison of resting-state networks. Frontiers in Systems Neuroscience, (FEBRUARY 2011), [2]. https://doi.org/10.3389/fnsys.2011.00002