Extracting brain disease-related connectome subgraphs by adaptive dense subgraph discovery.

ABSTRACT: 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 infer

SUBMITTER: Wu Q 

PROVIDER: S-EPMC10396394 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature