Neuroimaging measures of the brain's white matter connections can enable the prediction of non-imaging phenotypes, such as demographic and cognitive measures. Existing works have investigated traditional microstructure and connectivity measures from diffusion MRI tractography, without considering the shape of the connections reconstructed by tractography. In this paper, we investigate the potential of fiber tract shape features for predicting non-imaging phenotypes, both individually and in combination with traditional features. We focus on three basic shape features: length, diameter, and elongation. Two different prediction methods are used, including a traditional regression method and a deep-learning-based prediction method. Experiments use an efficient two-stage fusion strategy for prediction using microstructure, connectivity, and shape measures. To reduce predictive bias due to brain size, normalized shape features are also investigated. Experimental results on the Human Connectome Project (HCP) young adult dataset (n=1065) demonstrate that individual shape features are predictive of non-imaging phenotypes. When combined with microstructure and connectivity features, shape features significantly improve performance for predicting the cognitive score TPVT (NIH Toolbox picture vocabulary test). Overall, this study demonstrates that the shape of fiber tracts contains useful information for the description and study of the living human brain using machine learning.
翻译:脑白物质连接的神经成像度量可以预测非成形型型型型,如人口和认知计量等; 现有工作调查了通过传播MRI成像法进行的传统微型结构和连接度量,没有考虑通过成像法重建的连接的形状; 在本文中,我们调查纤维成形特性对于预测非成形型型型型型型型的个别和与传统特征相结合的潜力; 我们侧重于三种基本形状特征:长度、直径和延长。 使用了两种不同的预测方法, 包括传统的回归法和深层学习预测方法。 实验使用高效的两阶段融合战略来利用微结构、连通性和形状测量等措施进行预测。 为了减少因大脑大小而重建的预测偏差,还调查了普通形状特征。 人类连接项目(HCP)年轻成人数据集(n=1065)的实验结果显示, 个体成形特征可以预测非成型型型型。 与微结构和连接性特征相结合时, 将显著地改进了利用微结构、深学习方法进行预测的两阶段性业绩, 展示了TPV系统结构的人体测试。</s>