Prediction of individual brain maturity using fMRI

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

Research output: Contribution to journalArticle

1066 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1358-1361
Number of pages4
JournalScience
Volume329
Issue number5997
DOIs
StatePublished - Sep 10 2010

ASJC Scopus subject areas

  • General

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