The brain's white matter (WM) undergoes developmental and degenerative processes during the human lifespan. To investigate the relationship between WM anatomical regions and age, we study diffusion magnetic resonance imaging tractography that is finely parcellated into fiber clusters in the deep, superficial, and cerebellar WM. We propose a deep-learning-based age prediction model that leverages large convolutional kernels and inverted bottlenecks. We improve performance using novel discrete multi-faceted mix data augmentation and a novel prior-knowledge-based loss function that encourages age predictions in the expected range. We study a dataset of 965 healthy young adults (22-37 years) derived from the Human Connectome Project (HCP). Experimental results demonstrate that the proposed model achieves a mean absolute error of 2.59 years and outperforms compared methods. We find that the deep WM is the most informative for age prediction in this cohort, while the superficial WM is the least informative. Overall, the most predictive WM tracts are the thalamo-frontal tract from the deep WM and the intracerebellar input and Purkinje tract from the cerebellar WM.

翻译：人脑白质在生命周期中经历发育和退化过程。为探究白质解剖区域与年龄的关系，我们研究了扩散磁共振成像纤维束成像技术，该技术将白质精细划分为深层白质、浅层白质和小脑白质的纤维簇。我们提出一种基于深度学习的年龄预测模型，该模型采用大卷积核和倒瓶颈结构。通过新颖的离散多面混合数据增强方法及基于先验知识的损失函数（该函数约束年龄预测值处于预期范围内），我们提升了模型性能。我们研究了来自人类连接组计划的965名健康年轻成年人（22-37岁）数据集。实验结果表明，所提模型实现了2.59年的平均绝对误差，优于对比方法。我们发现，在该队列中，深层白质对年龄预测的信息量最大，而浅层白质的信息量最小。总体而言，最具预测性的白质纤维束包括：深层白质的丘脑-额叶束、小脑白质的小脑内输入纤维束及浦肯野纤维束。