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Prediction of Individual Brain Maturity Using fMRI

2010; American Association for the Advancement of Science; Volume: 329; Issue: 5997 Linguagem: Inglês

10.1126/science.1194144

1095-9203

Nico U.F. Dosenbach, Binyam Nardos, Alexander L. Cohen, Damien A. Fair, Jonathan D. Power, Jessica A. Church, Scott M. Nelson, Gagan S. Wig, Alecia C. Vogel, Christina N. Lessov‐Schlaggar, Kelly A. Barnes, Joseph W. Dubis, Eric Feczko, Rebecca S. Coalson, John R. Pruett, Deanna M. Barch, Steven E. Petersen, Bradley L. Schlaggar,

Advanced Neuroimaging Techniques and Applications

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.