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The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity that may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative\u00a0approaches.<\/jats:p>","DOI":"10.1111\/biom.13235","type":"journal-article","created":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T09:53:20Z","timestamp":1580982800000},"page":"1120-1132","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease"],"prefix":"10.1093","volume":"76","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3316-0468","authenticated-orcid":false,"given":"Christine B.","family":"Peterson","sequence":"first","affiliation":[{"name":"Department of Biostatistics MD Anderson Cancer Center Houston Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nathan","family":"Osborne","sequence":"additional","affiliation":[{"name":"Department of Statistics Rice University Houston Texas"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9150-8552","authenticated-orcid":false,"given":"Francesco C.","family":"Stingo","sequence":"additional","affiliation":[{"name":"Department of Statistics Computer Science Applications \u201cG. Parenti\u201d University of Florence Florence Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierrick","family":"Bourgeat","sequence":"additional","affiliation":[{"name":"Australian eHealth Research Centre CSIRO Health and Biosecurity Herston Queensland Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James D.","family":"Doecke","sequence":"additional","affiliation":[{"name":"Australian eHealth Research Centre CSIRO Health and Biosecurity Herston Queensland Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7360-5321","authenticated-orcid":false,"given":"Marina","family":"Vannucci","sequence":"additional","affiliation":[{"name":"Department of Statistics Rice University Houston 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