Chen Shuo, Zhang Yuan, Wu Qiong, Bi Chuan, Kochunov Peter, Hong L Elliot
Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, 660 W. Redwood Street Baltimore, MD 21201, USA and Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, 55 Wade Avenue, Catonsville, MD 21228, USA.
Department of Statistics, Ohio State University, 1958 Neil Ave, Columbus, OH 43210, USA.
Biostatistics. 2024 Apr 15;25(2):541-558. doi: 10.1093/biostatistics/kxad007.
Whole-brain connectome data characterize the connections among distributed neural populations as a set of edges in a large network, and neuroscience research aims to systematically investigate associations between brain connectome and clinical or experimental conditions as covariates. A covariate is often related to a number of edges connecting multiple brain areas in an organized structure. However, in practice, neither the covariate-related edges nor the structure is known. Therefore, the understanding of underlying neural mechanisms relies on statistical methods that are capable of simultaneously identifying covariate-related connections and recognizing their network topological structures. The task can be challenging because of false-positive noise and almost infinite possibilities of edges combining into subnetworks. To address these challenges, we propose a new statistical approach to handle multivariate edge variables as outcomes and output covariate-related subnetworks. We first study the graph properties of covariate-related subnetworks from a graph and combinatorics perspective and accordingly bridge the inference for individual connectome edges and covariate-related subnetworks. Next, we develop efficient algorithms to exact covariate-related subnetworks from the whole-brain connectome data with an $\ell_0$ norm penalty. We validate the proposed methods based on an extensive simulation study, and we benchmark our performance against existing methods. Using our proposed method, we analyze two separate resting-state functional magnetic resonance imaging data sets for schizophrenia research and obtain highly replicable disease-related subnetworks.
全脑连接组数据将分布式神经群体之间的连接表征为一个大型网络中的一组边，神经科学研究旨在系统地研究脑连接组与作为协变量的临床或实验条件之间的关联。协变量通常与以有组织结构连接多个脑区的一些边相关。然而，在实践中，与协变量相关的边及其结构都是未知的。因此，对潜在神经机制的理解依赖于能够同时识别与协变量相关的连接并识别其网络拓扑结构的统计方法。由于假阳性噪声以及边组合成子网的几乎无限可能性，该任务可能具有挑战性。为了应对这些挑战，我们提出了一种新的统计方法，将多元边变量作为结果进行处理，并输出与协变量相关的子网。我们首先从图论和组合学的角度研究与协变量相关的子网的图属性，并相应地在个体连接组边和与协变量相关的子网的推断之间架起桥梁。接下来，我们开发了高效的算法，通过$\ell_0$范数惩罚从全脑连接组数据中精确提取与协变量相关的子网。我们基于广泛的模拟研究验证了所提出的方法，并将我们的性能与现有方法进行了基准测试。使用我们提出的方法，我们分析了两个用于精神分裂症研究的独立静息态功能磁共振成像数据集，并获得了高度可重复的疾病相关子网。
