Resting-state fMRI can reliably map neural networks in children

Moriah E. Thomason, Emily L. Dennis, Anand A. Joshi, Shantanu H. Joshi, Ivo D. Dinov, Catie Chang, Melissa L. Henry, Rebecca F. Johnson, Paul M. Thompson, Arthur W. Toga, Gary H. Glover, John D. Van Horn, Ian H. Gotlib

Research output: Contribution to journalArticlepeer-review

136 Scopus citations

Abstract

Resting-state MRI (rs-fMRI) is a powerful procedure for studying whole-brain neural connectivity. In this study we provide the first empirical evidence of the longitudinal reliability of rs-fMRI in children. We compared rest-retest measurements across spatial, temporal and frequency domains for each of six cognitive and sensorimotor intrinsic connectivity networks (ICNs) both within and between scan sessions. Using Kendall's. W, concordance of spatial maps ranged from .60 to .86 across networks, for various derived measures. The Pearson correlation coefficient for temporal coherence between networks across all Time 1-Time 2 (T1/T2) z-converted measures was .66 (p< .001). There were no differences between T1/T2 measurements in low-frequency power of the ICNs. For the visual network, within-session T1 correlated with the T2 low-frequency power, across participants. These measures from resting-state data in children were consistent across multiple domains (spatial, temporal, and frequency). Resting-state connectivity is therefore a reliable method for assessing large-scale brain networks in children.

Original languageEnglish
Pages (from-to)165-175
Number of pages11
JournalNeuroImage
Volume55
Issue number1
DOIs
StatePublished - 1 Mar 2011
Externally publishedYes

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