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 tensor image data.

翻译：我们对于大脑结构及其与人类特征之间关系的理解在很大程度上取决于我们如何代表结构连接体。标准做法将大脑分为感兴趣的区域(ROIs),并将连接体代表成一个具有细胞测量相互连接的细胞的相邻矩阵。然后,统计分析在很大程度上受(大为任意的)选择内部连接体的驱动。在本篇文章中,我们建议对大脑连接体进行新的地形学代表,将纤维端点组合在一起,以界定数据适应性包件,旨在解释个人之间的差异并预测人类特征。这种表述导致主要分解分析(PPA),通过基于在人口层面捕捉连接的纤维捆绑系统建立成个体脑连接体,代表个体脑连接体。PPA降低了主观性,为统计分析提供了便利。我们通过人类连接体项目的数据应用来说明拟议的方法,并表明PPA联系单位在基于古典连接体的状态方法预测人类特征和预测能力方面提高了能力,同时大大改进了孔径和保持了可传播性。我们GPA软件包可以公开得到。