Chen Yuqian, Zhang Fan, Wang Meng, Zekelman Leo R, Cetin-Karayumak Suheyla, Xue Tengfei, Zhang Chaoyi, Song Yang, Rushmore Jarrett, Makris Nikos, Rathi Yogesh, Cai Weidong, O'Donnell Lauren J
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, PR China.
Med Image Anal. 2025 Apr;101:103476. doi: 10.1016/j.media.2025.103476. Epub 2025 Jan 20.
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network design. We introduce TractGraphFormer, a hybrid Graph CNN-Transformer deep learning framework tailored for diffusion MRI tractography. This model leverages local anatomical characteristics and global feature dependencies of white matter structures. The Graph CNN module captures white matter geometry and grey matter connectivity to aggregate local features from anatomically similar white matter connections, while the Transformer module uses self-attention to enhance global information learning. Additionally, TractGraphFormer includes an attention module for interpreting predictive white matter connections. We apply TractGraphFormer to tasks of sex and age prediction. TractGraphFormer shows strong performance in large datasets of children (n = 9345) and young adults (n = 1065). Overall, our approach suggests that widespread connections in the WM are predictive of the sex and age of an individual. For each prediction task, consistent predictive anatomical tracts are identified across the two datasets. The proposed approach highlights the potential of integrating local anatomical information and global feature dependencies to improve prediction performance in machine learning with diffusion MRI tractography.
人们越来越多地使用深度神经网络来研究大脑连接与非成像表型之间的关系。然而，在卷积网络设计中，大脑白质网络的局部和全局特性常常被忽视。我们引入了TractGraphFormer，这是一种专门为扩散磁共振成像纤维束成像量身定制的图卷积神经网络（Graph CNN）与Transformer的混合深度学习框架。该模型利用了白质结构的局部解剖特征和全局特征依赖性。图卷积神经网络模块捕捉白质几何形状和灰质连通性，以聚合来自解剖学上相似的白质连接的局部特征，而Transformer模块则使用自注意力来增强全局信息学习。此外，TractGraphFormer还包括一个注意力模块，用于解释预测性白质连接。我们将TractGraphFormer应用于性别和年龄预测任务。TractGraphFormer在儿童（n = 9345）和年轻人（n = 1065）的大型数据集中表现出强大的性能。总体而言，我们的方法表明，白质中的广泛连接可预测个体的性别和年龄。对于每个预测任务，在两个数据集中都识别出了一致的预测性解剖纤维束。所提出的方法突出了整合局部解剖信息和全局特征依赖性以提高扩散磁共振成像纤维束成像机器学习预测性能的潜力。
