Detecting and testing altered brain connectivity networks with \(k\)-partite network topology

DOI10.1016/j.csda.2019.06.007OpenAlexW2958669495WikidataQ98649296 ScholiaQ98649296MaRDI QIDQ2008002

F. DuBois Bowman, Shuo Chen, Yishi Xing

Publication date: 22 November 2019

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442212

zbMATH Keywords

connectivityParkinson's disease\(k\)-partite graphbrain network statisticsnetwork topological statistics

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biomedical imaging and signal processing (92C55) Paired and multiple comparisons; multiple testing (62J15)

Uses Software

• IsoRank
• IsoRankN
• Brain Connectivity Toolbox

Cites Work

• Unnamed Item
• Learning population and subject-specific brain connectivity networks via mixed neighborhood selection
• The control of the false discovery rate in multiple testing under dependency.
• Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
• A Direct Approach to False Discovery Rates
• Estimation of Entropy and Mutual Information
• Applied Spatial Statistics for Public Health Data
• A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data