Brain aging is a regional phenomenon, a facet that remains relatively under-explored within the realm of brain age prediction research using machine learning methods. Voxel-level predictions can provide localized brain age estimates that can provide granular insights into the regional aging processes. This is essential to understand the differences in aging trajectories in healthy versus diseased subjects. In this work, a deep learning-based multitask model is proposed for voxel-level brain age prediction from T1-weighted magnetic resonance images. The proposed model outperforms the models existing in the literature and yields valuable clinical insights when applied to both healthy and diseased populations. Regional analysis is performed on the voxel-level brain age predictions to understand aging trajectories of known anatomical regions in the brain and show that there exist disparities in regional aging trajectories of healthy subjects compared to ones with underlying neurological disorders such as Dementia and more specifically, Alzheimer's disease. Our code is available at https://github.com/nehagianchandani/Voxel-level-brain-age-prediction.

翻译：脑衰老是一种区域性现象，但这一特性在基于机器学习方法的脑龄预测研究中仍相对未被充分探索。体素级预测能够提供局部脑龄估算，从而深入揭示区域性脑衰老进程的细节，这对于理解健康与患病个体在衰老轨迹上的差异至关重要。本研究提出一种基于深度学习的多任务模型，用于从T1加权磁共振图像实现体素级脑龄预测。该模型性能优于现有文献中的相关模型，并在应用于健康及患病人群时提供了具有临床价值的见解。通过对体素级脑龄预测结果进行区域性分析，我们解析了大脑已知解剖区域的衰老轨迹，并发现与患有神经退行性疾病（特别是阿尔茨海默病）的受试者相比，健康个体的区域性脑衰老轨迹存在差异。我们的代码已开源于https://github.com/nehagianchandani/Voxel-level-brain-age-prediction。