Prediction of Individual Brain Maturity Using fMRI
Connectivity Map of the Brain
The growing appreciation that clinically abnormal behaviors in children and adolescents may be influenced or perhaps even initiated by developmental miscues has stoked an interest in mapping normal human brain maturation. Several groups have documented changes in gray and white matter using structural and functional magnetic resonance imaging (fMRI) in cross-sectional and longitudinal studies. Dosenbach et al. (p. 1358) developed an index of resting-state functional connectivity (that is, how tightly neuronal activities in distinct brain regions are correlated while the subject is at rest or even asleep) from analyses of three independent data sets (each based on fMRI scans of 150 to 200 individuals from ages 6 to 35 years old). Long-range connections increased with age and short-range connections decreased, indicating that networks become sparser and sharper with brain maturation.
Abstract
Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals’ brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain’s major functional networks.
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Information & Authors
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Published In
Science
Volume 329 | Issue 5997
10 September 2010
10 September 2010
Copyright
Copyright © 2010, American Association for the Advancement of Science.
Submission history
Received: 23 June 2010
Accepted: 4 August 2010
Published in print: 10 September 2010
Acknowledgments
This work was supported by NIH grants NS55582, NS053425, HD057076, and NS00169011 (B.L.S.); NS51281, NS32979, NS41255, and NS46424 (S.E.P.); DA027046 (C.N.L.-S.); EY16336 (J.R.P.); and MH62130 (D.M.B.) and by the John Merck Scholars Fund (B.L.S.), Burroughs-Wellcome Fund (B.L.S.), Dana Foundation (B.L.S.), Ogle Family Fund (B.L.S.), McDonnell Center (S.E.P. and B.L.S.), Simons Foundation (S.E.P.), American Hearing Research Foundation (J. E. C. Lieu), and Diabetes Research Center at Washington University (T. G. Hershey). We thank J. E. C. Lieu, C. E. Pizoli, and T. G. Hershey for providing data and F. M. Miezin, J. Harwell, A. Z. Snyder, and H. M. Lugar for help with data analysis.
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