Li Didong, Nguyen Phuc, Zhang Zhengwu, Dunson David
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Department of Statistical Science, Duke University, Durham, NC, United States.
Front Neurosci. 2023 Oct 13;17:1200373. doi: 10.3389/fnins.2023.1200373. eCollection 2023.
The brain structural connectome is generated by a collection of white matter fiber bundles constructed from diffusion weighted MRI (dMRI), acting as highways for neural activity. There has been abundant interest in studying how the structural connectome varies across individuals in relation to their traits, ranging from age and gender to neuropsychiatric outcomes. After applying tractography to dMRI to get white matter fiber bundles, a key question is how to represent the brain connectome to facilitate statistical analyses relating connectomes to traits. The current standard divides the brain into regions of interest (ROIs), and then relies on an (AM) representation. Each cell in the AM is a measure of connectivity, e.g., number of fiber curves, between a pair of ROIs. Although the AM representation is intuitive, a disadvantage is the high-dimensionality due to the large number of cells in the matrix. This article proposes a simpler tree representation of the brain connectome, which is motivated by ideas in computational topology and takes topological and biological information on the cortical surface into consideration. We demonstrate that our tree representation preserves useful information and interpretability, while reducing dimensionality to improve statistical and computational efficiency. Applications to data from the Human Connectome Project (HCP) are considered and code is provided for reproducing our analyses.
脑结构连接组由一组基于扩散加权磁共振成像（dMRI）构建的白质纤维束生成，这些纤维束充当神经活动的“高速公路”。人们对研究结构连接组如何因个体特征（从年龄、性别到神经精神疾病结果）的不同而变化有着浓厚兴趣。在对dMRI应用纤维束成像以获得白质纤维束后，一个关键问题是如何表示脑连接组，以便于进行将连接组与特征相关联的统计分析。当前的标准是将大脑划分为感兴趣区域（ROI），然后依赖于邻接矩阵（AM）表示。AM中的每个单元都是一对ROI之间连接性的度量，例如纤维曲线的数量。虽然AM表示直观，但一个缺点是由于矩阵中单元数量众多而导致维度很高。本文提出了一种更简单的脑连接组树表示方法，该方法受计算拓扑学思想的启发，并考虑了皮质表面的拓扑和生物学信息。我们证明，我们的树表示保留了有用的信息和可解释性，同时降低了维度以提高统计和计算效率。我们考虑了对人类连接组计划（HCP）数据的应用，并提供了用于重现我们分析的代码。
