Journal
SCIENCE
Volume 329, Issue 5997, Pages 1358-1361Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.1194144
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Funding
- NIH [NS55582, NS053425, HD057076, NS00169011, NS51281, NS32979, NS41255, NS46424, DA027046]
- John Merck Scholars Fund
- Burroughs-Wellcome Fund
- Dana Foundation
- Ogle Family Fund
- McDonnell Center
- Simons Foundation
- American Hearing Research Foundation
- Diabetes Research Center at Washington University
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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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