PPA：面向脑连接组与多性状的主分区分析
PPA: Principal Parcellation Analysis for Brain Connectomes and Multiple Traits
AI总结：
针对传统脑连接组分析依赖ROI选择、主观性强的问题，提出主分区分析PPA方法，通过纤维端点聚类构建自适应分区和纤维束基向量表征连接组，在HCP数据上的性状预测效能优于经典方法，且更简约可解释。
摘要：
我们对大脑结构及其与人类性状关联的理解，很大程度上取决于我们对结构连接组的表征方式。标准做法是将大脑划分为感兴趣区（ROIs），并将连接组表示为邻接矩阵，矩阵元素衡量成对ROIs之间的连接强度。后续统计分析在很大程度上受ROI选择（这种选择基本具有任意性）的驱动。本文提出一种基于纤维束追踪的新型脑连接组表征方法，该方法通过聚类纤维端点定义数据自适应分区，该分区旨在解释个体间差异并预测人类性状。基于这种表征衍生出主分区分析（PPA），该方法通过在纤维束基系统上构建的成分向量来表征个体脑连接组，该基系统可捕捉群体层面的连接性。PPA降低了主观性，为统计分析提供了便利。我们通过应用于人类连接组计划（HCP）的数据对所提方法进行了验证，结果表明，与基于经典连接组的前沿方法相比，PPA连接组在预测人类性状方面提升了效能，同时大幅提高了简约性并保持了可解释性。我们的PPA包已在GitHub上公开，可常规用于扩散图像数据。
英文摘要：
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a novel tractography-based representation of brain connectomes, which clusters fiber endpoints to define a data adaptive parcellation targeted to explain variation among individuals and predict human traits. This representation leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA reduces subjectivity and facilitates statistical analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
