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：脑白质连接的神经影像学测量指标可用于预测非影像表型（如人口统计学和认知测量指标）。现有研究已探究弥散张量成像纤维束成像技术获得的传统微观结构和连接性指标，但未考虑纤维束追踪重建的纤维束形状特征。本文系统研究了纤维束形状特征在预测非影像表型中的潜力，包括单独使用及与传统特征组合应用。我们聚焦三种基础形状特征：长度、直径和伸长率。采用两种不同预测方法：传统回归方法和基于深度学习的预测方法。实验采用高效的两阶段融合策略，整合微观结构、连接性和形状特征进行预测。为降低脑尺寸引起的预测偏差，还探究了归一化形状特征。基于人类连接组计划（HCP）青年成人数据集（n=1065）的实验结果表明，单一形状特征即可有效预测非影像表型。当与微观结构和连接性特征联合使用时，形状特征可显著提升认知评分TPVT（NIH工具箱图片词汇测试）的预测性能。本研究证实，纤维束形状特征包含描述和研究活体人脑的重要信息，可通过机器学习方法进行有效利用。