{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:27:48Z","timestamp":1777397268712,"version":"3.51.4"},"reference-count":61,"publisher":"SAGE Publications","issue":"1_suppl","license":[{"start":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T00:00:00Z","timestamp":1746316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T00:00:00Z","timestamp":1746316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The early detection of Alzheimer\u2019s disease (AD) requires an understanding of the relationships between a wide range of features. Conditional independencies and partial correlations are suitable measures for these relationships, because they can identify the effects of confounding and mediating variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To estimate conditional dependencies and partial correlations between relevant features in AD using a Bayesian approach to Gaussian copula graphical models (GCGMs). This approach has two key advantages. First, it includes binary, discrete, and continuous variables. Second, it quantifies the uncertainty of the estimates. Despite these advantages, Bayesian GCGMs have not been applied to AD research yet.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We design a GCGM to find the conditional dependencies and partial correlations among brain-region specific gray matter volume and glucose uptake, amyloid-beta levels, demographic information, and cognitive test scores. We applied our model to\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                          <mml:mn>1<\/mml:mn>\n                          <mml:mspace width=\".1em\"\/>\n                          <mml:mn>022<\/mml:mn>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      participants, including healthy and cognitively impaired, across different stages of AD.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We found that aging reduces cognition through three indirect pathways: hippocampal volume loss, posterior cingulate cortex (PCC) volume loss, and amyloid-beta accumulation. We found a positive partial correlation between being woman and cognition, but also discovered four indirect pathways that dampen this association in women: lower hippocampal volume, lower PCC volume, more amyloid-beta accumulation, and less education. We found limited relations between brain-region specific glucose uptake and cognition, but discovered that the hippocampus and PCC volumes are related to cognition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study shows that the use of GCGMs offers valuable insights into AD pathogenesis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877251337944","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T02:31:26Z","timestamp":1746412286000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling Alzheimer\u2019s disease: Bayesian copula graphical model from demographic, cognitive, and neuroimaging data"],"prefix":"10.1177","volume":"108","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3715-3937","authenticated-orcid":false,"given":"Lucas","family":"Vogels","sequence":"first","affiliation":[{"name":"Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9538-0648","authenticated-orcid":false,"given":"Reza","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2268-7486","authenticated-orcid":false,"given":"Marit","family":"Schoonhoven","sequence":"additional","affiliation":[{"name":"Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7472-7032","authenticated-orcid":false,"given":"\u015e Ilker","family":"Birbil","sequence":"additional","affiliation":[{"name":"Amsterdam Business School, University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3353-3167","authenticated-orcid":false,"given":"Martin","family":"Dyrba","sequence":"additional","affiliation":[{"name":"German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,5,4]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(24)01296-0"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/molecules25245789"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.116607"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-215196"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.31887\/DCNS.2013.15.3\/osporns"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.0010042"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.nec.2010.12.004"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1379404"},{"key":"e_1_3_5_10_2","first-page":"1","article-title":"Brain functional and effective connectivity based on electroencephalography recordings: a review","volume":"43","author":"Cao J","year":"2021","unstructured":"Cao J, Zhao Y, Shan X, et al. Brain functional and effective connectivity based on electroencephalography recordings: a review. Hum Brain Mapp 2021; 43: 1\u201320.","journal-title":"Hum Brain Mapp"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.11.062"},{"key":"e_1_3_5_12_2","volume-title":"Principles and practice of structural equation modeling","author":"Kline R","year":"2023","unstructured":"Kline R. Principles and practice of structural equation modeling. New York: The Guilford Press, 2023."},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwm326"},{"key":"e_1_3_5_14_2","first-page":"422","article-title":"Polygenic mediation analysis of Alzheimer\u2019s disease implicated intermediate amyloid imaging phenotypes","volume":"2020","author":"Yingxuan E","year":"2021","unstructured":"Yingxuan E, Yao X, Liu K, et al. Polygenic mediation analysis of Alzheimer\u2019s disease implicated intermediate amyloid imaging phenotypes. AMIA Annu 2021; 2020: 422\u2013431.","journal-title":"AMIA Annu"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-82776-y"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9425"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-022-01437-6"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-023-01233-z"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-59669-x"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1002\/alz.12923"},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198522195.001.0001"},{"key":"e_1_3_5_22_2","volume-title":"Probabilistic graphical models: principles and techniques","author":"Koller D","year":"2009","unstructured":"Koller D, Friedman N. Probabilistic graphical models: principles and techniques. Cambridge, MA: MIT Press, 2009."},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2020.00099"},{"key":"e_1_3_5_24_2","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2015.00132"},{"key":"e_1_3_5_25_2","doi-asserted-by":"publisher","DOI":"10.1177\/0271678X15622465"},{"key":"e_1_3_5_26_2","first-page":"14","article-title":"Sex and gender differences in Alzheimer\u2019s disease dementia","volume":"35","author":"Mielke M","year":"2018","unstructured":"Mielke M. Sex and gender differences in Alzheimer\u2019s disease dementia. Psychiatr Times 2018; 35: 14\u201317.","journal-title":"Psychiatr Times"},{"key":"e_1_3_5_27_2","doi-asserted-by":"crossref","unstructured":"Mohammadi A Schoonhoven M Vogels L et al. Large-scale Bayesian structure learning for Gaussian graphical models using marginal pseudo-likelihood. 2024 Unpublished manuscript Arxiv 2307.00127.","DOI":"10.1214\/25-BA1561"},{"key":"e_1_3_5_28_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2024.2395504"},{"key":"e_1_3_5_29_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-161197"},{"key":"e_1_3_5_30_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004534"},{"key":"e_1_3_5_31_2","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOAS397"},{"key":"e_1_3_5_32_2","doi-asserted-by":"publisher","DOI":"10.1111\/rssc.12171"},{"key":"e_1_3_5_33_2","doi-asserted-by":"publisher","DOI":"10.1162\/imag_a_00258"},{"key":"e_1_3_5_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-012-9186-z"},{"key":"e_1_3_5_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-012-9176-1"},{"key":"e_1_3_5_36_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.23018"},{"key":"e_1_3_5_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.021"},{"key":"e_1_3_5_38_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41583-019-0132-6"},{"key":"e_1_3_5_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2023.103393"},{"key":"e_1_3_5_40_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000004643"},{"key":"e_1_3_5_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.07.007"},{"key":"e_1_3_5_42_2","doi-asserted-by":"publisher","DOI":"10.1038\/jcbfm.1992.81"},{"key":"e_1_3_5_43_2","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxm045"},{"key":"e_1_3_5_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-92407-6"},{"key":"e_1_3_5_45_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2020.1817750"},{"key":"e_1_3_5_46_2","first-page":"2295","article-title":"The nonparanormal: semiparametric estimation of high dimensional undirected graphs","volume":"10","author":"Liu H","year":"2009","unstructured":"Liu H, Lafferty J, Wasserman L. The nonparanormal: semiparametric estimation of high dimensional undirected graphs. J Mach Learn Res 2009; 10: 2295\u20132328.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_5_47_2","doi-asserted-by":"publisher","DOI":"10.1214\/07-AOAS107"},{"key":"e_1_3_5_48_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v089.i03"},{"key":"e_1_3_5_49_2","doi-asserted-by":"publisher","DOI":"10.3390\/molecules27041210"},{"key":"e_1_3_5_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2016.01.002"},{"key":"e_1_3_5_51_2","doi-asserted-by":"publisher","DOI":"10.1097\/WAD.0000000000000187"},{"key":"e_1_3_5_52_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2023.1105620"},{"key":"e_1_3_5_53_2","doi-asserted-by":"publisher","DOI":"10.1002\/alz.13403"},{"key":"e_1_3_5_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2018.06.030"},{"key":"e_1_3_5_55_2","doi-asserted-by":"publisher","DOI":"10.1038\/nri.2016.90"},{"key":"e_1_3_5_56_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25320"},{"key":"e_1_3_5_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2016.08.016"},{"key":"e_1_3_5_58_2","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhl075"},{"key":"e_1_3_5_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2009.07.002"},{"key":"e_1_3_5_60_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181c3f293"},{"key":"e_1_3_5_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2007.05.016"},{"key":"e_1_3_5_62_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpet.2013.10.006"}],"container-title":["Journal of Alzheimer\u2019s Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/13872877251337944","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/13872877251337944","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/13872877251337944","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T10:10:36Z","timestamp":1777371036000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/13872877251337944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,4]]},"references-count":61,"journal-issue":{"issue":"1_suppl","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1177\/13872877251337944"],"URL":"https:\/\/doi.org\/10.1177\/13872877251337944","relation":{},"ISSN":["1387-2877","1875-8908"],"issn-type":[{"value":"1387-2877","type":"print"},{"value":"1875-8908","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,4]]}}}