论文标题
使用脑白质特征模型并预测健康受试者的年龄和性别：一种深度学习方法
Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach
论文作者
论文摘要
人脑的白质（WM）结构对科学界引起了极大的兴趣。扩散MRI提供了一种强大的工具，可以无创地描述大脑WM结构。为了有可能监测与年龄相关的变化，并研究了与性别相关的大脑结构在大脑连接组与健康受试者的年龄和性别之间的映射上的差异，我们提取了基于纤维群的扩散特征，并通过新颖的综合神经网络分类器来预测性别和年龄。我们对人类连接项目（HCP）的年轻成人数据集进行了实验，并表明我们的模型在性别预测中达到了94.82％的精度，而年龄预测的2.51岁。我们还表明，分数各向异性（FA）是性别的最预测性，而纤维的数量是年龄的最终预测性，不同特征的组合可以改善模型性能。
The human brain's white matter (WM) structure is of immense interest to the scientific community. Diffusion MRI gives a powerful tool to describe the brain WM structure noninvasively. To potentially enable monitoring of age-related changes and investigation of sex-related brain structure differences on the mapping between the brain connectome and healthy subjects' age and sex, we extract fiber-cluster-based diffusion features and predict sex and age with a novel ensembled neural network classifier. We conduct experiments on the Human Connectome Project (HCP) young adult dataset and show that our model achieves 94.82% accuracy in sex prediction and 2.51 years MAE in age prediction. We also show that the fractional anisotropy (FA) is the most predictive of sex, while the number of fibers is the most predictive of age and the combination of different features can improve the model performance.
