We propose a geometric deep-learning-based framework, TractGeoNet, for
performing regression using diffusion magnetic resonance imaging (dMRI)
tractography and associated pointwise tissue microstructure measurements. By
employing a point cloud representation, TractGeoNet can directly utilize
pointwise tissue microstructure and positional information from all points
within a fiber tract. To improve regression performance, we propose a novel
loss function, the Paired-Siamese Regression loss, which encourages the model
to focus on accurately predicting the relative differences between regression
label scores rather than just their absolute values. In addition, we propose a
Critical Region Localization algorithm to identify highly predictive anatomical
regions within the white matter fiber tracts for the regression task. We
evaluate the effectiveness of the proposed method by predicting individual
performance on two neuropsychological assessments of language using a dataset
of 20 association white matter fiber tracts from 806 subjects from the Human
Connectome Project. The results demonstrate superior prediction performance of
TractGeoNet compared to several popular regression models. Of the twenty tracts
studied, we find that the left arcuate fasciculus tract is the most highly
predictive of the two studied language performance assessments. The localized
critical regions are widespread and distributed across both hemispheres and all
cerebral lobes, including areas of the brain considered important for language
function such as superior and anterior temporal regions, pars opercularis, and
precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric
deep learning to enhance the study of the brain's white matter fiber tracts and
to relate their structure to human traits such as language performance.

我们提出了一种基于几何深度学习的框架 TractGeoNet，用于使用扩散磁共振成像（dMRI）束索术和相关点微结构测量进行回归。该方法利用点云表示，直接利用纤维束内所有点的点微结构和位置信息。为改善回归性能，我们提出了一种新的损失函数 Paired-Siamese Regression loss，鼓励模型关注准确预测回归标签得分之间的相对差异，而不仅仅是绝对值。此外，我们提出了一种关键区域定位算法，用于识别白质纤维束内高度预测的解剖区域。通过使用来自人体连接组项目中 806 位受试者的 20 个关联白质纤维束的数据集，我们评估了所提方法的有效性，并证明了 TractGeoNet 相对于几种常见回归模型具有优越的预测性能。我们发现，在所研究的二十个纤维束中，左弓状束是两个语言性能评估中最具预测性的。定位的关键区域广泛分布于两个半球和所有大脑叶，包括被认为对语言功能重要的脑区，如颞叶上和前部区域、操作小叶和顶前回。总体而言，TractGeoNet 展示了几何深度学习增强研究大脑白质纤维束并将其结构与人类特征（如语言性能）相关的潜力。