旋回作用
人类连接体项目
平均绝对误差
特征(语言学)
模式识别(心理学)
公制(单位)
计算机科学
神经影像学
人工智能
脑形态计量学
估计
索引(排版)
曲率
磁共振成像
统计
数学
均方误差
大脑皮层
神经科学
生物
医学
功能连接
管理
经济
哲学
万维网
放射科
语言学
运营管理
几何学
作者
Xia Liu,Iman Beheshti,Weihao Zheng,Yongchao Li,Shan Li,Ziyang Zhao,Zhijun Yao,Bin Hu
标识
DOI:10.1016/j.compbiomed.2022.105285
摘要
Studying brain aging improves our understanding in differentiating typical and atypical aging. Directly utilizing traditional morphological features for brain age estimation did not show significant performance in healthy controls (HCs), which may be due to the negligence of the information of structural similarities among cortical regions. For this issue, the multi-feature-based network (MFN) built upon morphological features can be employed to describe these similarities. Based on this, we hypothesized that the MFN is more efficient and robust than traditional morphological features in brain age estimating. In this work, we used six different types of morphological features (i.e., cortical volume, cortical thickness, curvature index, folding index, local gyrification index, and surface area) to build individual MFN for brain age estimation. The efficacy of MFN was estimated on 2501 HCs with T1-weighted structural magnetic resonance imaging (sMRI) data and compared with traditional morphological features. We attained a mean absolute error (MAE) of 3.73 years using the proposed method on an independent test set, whereas a mean absolute error of 5.30 years was derived from morphological features. Our experimental results demonstrated that the MFN is an efficient and robust metric for estimating brain age.
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