Extracting brain disease-related connectome subgraphs by adaptive dense subgraph discovery.
Extracting brain disease-related connectome subgraphs by adaptive dense subgraph discovery.
复制标题
作者：
Wu Q;Huang X;Culbreth AJ;Waltz JA;Hong LE;Chen S
群体水平的脑连接组分析在神经精神病学研究中引起了越来越多的兴趣，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大小和位置的先验知识。此外，神经成像数据经常与大量噪声混合，这可能进一步模糊信息子网络检测。我们提出了一种基于似然的自适应密集子图发现（ADSD）模型，用于从群体水平的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都具有鲁棒性，因此可以更准确地发现潜在疾病相关的连接组子网络。我们开发了计算效率高的算法来实现新的ADSD目标函数，并推导了保证收敛性的理论结果。我们将提出的方法应用于精神分裂症研究的脑功能磁共振研究，并确定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著性网络中心连接异常。综合数据的分析也证明了ADSD方法在各种环境下的潜在子网检测性能优于现有方法。
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 is 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.
登录
查看更多内容
人脑网络组图谱：基于连接架构的新大脑图谱
DOI：
10.1093/cercor/bhw157
发表时间：
2016-08
期刊：
Cerebral cortex (New York, N.Y. : 1991)
影响因子：
--
作者：
Fan L;Li H;Zhuo J;Zhang Y;Wang J;Chen L;Yang Z;Chu C;Xie S;Laird AR;Fox PT;Eickhoff SB;Yu C;Jiang T
通讯作者：
Jiang T
影响因子：
4.8
作者：
Higgins, Ixavier A.;Kundu, Suprateek;Guo, Ying
通讯作者：
Guo, Ying
影响因子：
4.8
作者：
Adhikari, Bhim M.;Hong, L. Elliot;Kochunov, Peter
通讯作者：
Kochunov, Peter
影响因子：
1.8
作者：
Ginestet, Cedric E.;Li, Jun;Kolaczyk, Eric D.
通讯作者：
Kolaczyk, Eric D.
影响因子：
4.8
作者：
Nichols, TE;Holmes, AP
通讯作者：
Holmes, AP