期刊
SCIENCE
卷 329, 期 5997, 页码 1358-1361出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.1194144
关键词
-
资金
- 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
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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