Title
Resting-state fMRI can reliably map neural networks in children.
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'sW, 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.
Year
DOI
Venue
2011
10.1016/j.neuroimage.2010.11.080
NeuroImage
Keywords
Field
DocType
empirical evidence,low frequency,resting state,frequency domain,neural network
Brain mapping,Pearson product-moment correlation coefficient,Resting state fMRI,Cognitive psychology,Psychology,Concordance,Coherence (physics),Artificial intelligence,Artificial neural network,Cognition,Cartography,Machine learning
Journal
Volume
Issue
ISSN
55
1
1053-8119
Citations 
PageRank 
References 
23
1.24
13
Authors
13
Name
Order
Citations
PageRank
Moriah E. Thomason111716.56
Emily L. Dennis2729.43
Anand Joshi323523.06
Shantanu H. Joshi484550.12
Ivo D. Dinov558158.19
Catie Chang6104150.58
Melissa L. Henry7231.24
Rebecca F. Johnson8231.24
Paul Thompson93860321.32
Arthur W. Toga103128261.46
Gary H. Glover11131486.89
John D Van Horn1231628.50
Ian H. Gotlib13667.47