Group-level brain connectome analysis has attracted increasing interest in neuropsychiatric research with the goal of identifying connectomic subnetworks (subgraphs) that are systematically associated with brain disorders. However, extracting disease-related subnetworks from the whole brain connectome has been challenging, because no prior knowledge is available regarding the sizes and locations of the subnetworks. In addition, neuroimaging data are often mixed with substantial noise that can further obscure informative subnetwork detection. We propose a likelihood-based adaptive dense subgraph discovery (ADSD) model to extract disease-related subgraphs from the group-level whole brain connectome data. Our method is robust to both false positive and false negative errors of edge-wise inference and thus can lead to a more accurate discovery of latent disease-related connectomic subnetworks. We develop computationally efficient algorithms to implement the novel ADSD objective function and derive theoretical results to guarantee the convergence properties. We apply the proposed approach to a brain fMRI study for schizophrenia research and identify well-organized and biologically meaningful subnetworks that exhibit schizophrenia-related salience network centered connectivity abnormality. Analysis of synthetic data also demonstrates the superior performance of the ADSD method for latent subnetwork detection in comparison with existing methods in various settings.

中文翻译：

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通过自适应密集子图发现提取脑部疾病相关的连接组子图

群体水平的大脑连接组分析吸引了人们对神经精神病学研究越来越多的兴趣，其目标是识别与大脑疾病系统相关的连接组子网络（子图）。然而，从整个大脑连接组中提取与疾病相关的子网络一直具有挑战性，因为没有关于子网络的大小和位置的先验知识。此外，神经影像数据通常与大量噪声混合，这可能进一步模糊信息子网络检测。我们提出了一种基于可能性的自适应密集子图发现（ADSD）模型，用于从群体级全脑连接组数据中提取与疾病相关的子图。我们的方法对边缘推理的假阳性和假阴性错误具有鲁棒性，因此可以更准确地发现与潜在疾病相关的连接组子网络。我们开发计算高效的算法来实现新颖的 ADSD 目标函数并得出理论结果以保证收敛特性。我们将所提出的方法应用于精神分裂症研究的大脑功能磁共振成像研究，并识别出组织良好且具有生物学意义的子网络，这些子网络表现出与精神分裂症相关的显着性网络中心的连接异常。合成数据的分析还表明，与各种设置中的现有方法相比，ADSD 方法在潜在子网检测方面具有优越的性能。