ArticleBiometrics2022
Extracting brain disease-related connectome subgraphs by adaptive dense subgraph discovery.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Fast autoregressive model for multivariate dependent outcomes with application to lipidomics analysis for Alzheimer's disease and APOE-Computational statistics & data analysis · 2026Article
- Tobacco Smoking Functional Networks: A Whole-Brain Connectome Analysis in 24 539 Individuals.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2025Article
- A Graph-Based Statistical Approach to Identifying Functional Connectivity Networks in Patients with Traumatic Brain Injury.Iranian journal of child neurology · 2025Article
- Identifying covariate-related subnetworks for whole-brain connectome analysis.Biostatistics (Oxford, England) · 2024Article
- BNPower: a power calculation tool for data-driven network analysis for whole-brain connectome data.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Secondary analysis: Graph analysis of brain connectivity network in autism spectrum disorder.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2024Article
- Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping.Molecular and cellular neurosciences · 2023Article
- Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data.Computational statistics & data analysis · 2023Article
- Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity.NeuroImage · 2022Article
- Temporal-thalamic and cingulo-opercular connectivity in people with schizophrenia.NeuroImage. Clinical · 2021Article
- Mapping neurodegeneration with diffusion MRI: biomarkers, mechanisms, and clinical translation.Frontiers in neurologyReview
- A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures.Pacific Symposium on Biocomputing. Pacific Symposium on BiocomputingArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
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 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.