This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus—based on the hard ensemble (HE) algorithm—approximately minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. Computational stability, graph structure preservation, and biological relevance of the simplified representation resulting from the proposed parcellation are assessed on the Human Connectome Project data set. These aspects are assessed using (1) edge weight distribution divergence with respect to the dense connectome representation, (2) interhemispheric symmetry, (3) network characteristics' stability and agreement with respect to individually and anatomically parcellated networks, and (4) performance of the simplified connectome in a biological sex classification task. Ensemble parcellation was found to be highly stable with respect to subject sampling, outperforming anatomical atlases and other connectome-based parcellations in classification as well as preserving global connectome properties. The HE-based parcellation also showed a degree of symmetry comparable with anatomical atlases and a high degree of spatial contiguity without using explicit priors.

中文翻译：

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使用集成聚类优化连接驱动的大脑分组。

这项工作解决了构建统一的、拓扑最优的基于连接的大脑图谱的问题。所提出的方法在没有标签协议的情况下从单个分区聚合了一个整体分区，在足够灵活的单个分区和连接组平均拓扑结构的直观表示之间提供了平衡。这些方法利用先前提出的密集连接表示，首先对单个大脑执行基于图的分层分割，然后将各个分割聚合成共识分割。基于硬集成 (HE) 算法的共识搜索近似最小化集群成员距离的总和，有效地估计单个分割的伪卡歇均值。计算稳定性，图结构保存以及由提议的分割所产生的简化表示的生物学相关性在人类连接组项目数据集上进行评估。这些方面的评估使用 (1) 关于密集连接组表示的边缘权重分布发散，(2) 半球间对称性，(3) 网络特征的稳定性和关于单独和解剖分割网络的一致性，以及 (4) 的性能生物性别分类任务中的简化连接组。发现集成分割在受试者采样方面高度稳定，在分类方面优于解剖图谱和其他基于连接组的分割，并保留了全局连接组特性。