Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Science. 2010 Sep 10;329(5997):1358-61. doi: 10.1126/science.1194144.
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.
群组功能连接磁共振成像 (fcMRI) 研究记录了人类功能大脑成熟度在发育过程中可靠的变化。在这里,我们表明基于支持向量机的多元模式分析从 fcMRI 数据中提取了足够的信息,可以准确预测个体在整个发育过程中的大脑成熟度。仅使用来自 238 名正常发育志愿者(7 至 30 岁)的 5 分钟静息状态 fcMRI 数据,就可以预测个体的大脑成熟度作为功能连接成熟指数。由此产生的功能成熟曲线解释了样本方差的 55%,并遵循非线性渐近增长曲线形状。预测个体大脑成熟度的最大相对贡献来自削弱成人大脑主要功能网络之间的短程功能连接。