主成分分析
人脑
静息状态功能磁共振成像
特征(语言学)
构造(python库)
计算机科学
人工智能
模式识别(心理学)
函数主成分分析
默认模式网络
回归
组分(热力学)
相关性
功能连接
神经科学
机器学习
心理学
数学
统计
哲学
物理
几何学
程序设计语言
热力学
语言学
标识
DOI:10.3389/fnhum.2019.00062
摘要
The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age.
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