{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T02:22:07Z","timestamp":1755224527357,"version":"3.43.0"},"reference-count":87,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["RF1AG057895"],"award-info":[{"award-number":["RF1AG057895"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AG066184"],"award-info":[{"award-number":["R01AG066184"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AG070149"],"award-info":[{"award-number":["R01AG070149"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) lacks effective cures and is typically detected after substantial pathological changes have occurred, making intervention challenging. Alzheimer\u2019s disease (AD) intervention requires early detection of risk factors and understanding their complex interactions before substantial pathological changes manifest. Current research often examines individual risk factors in isolation, limiting our understanding of their combined effects. We present a novel multivariate analytical framework to simultaneously assess multiple AD risk factors using mouse models expressing human ApoE alleles. Our methodological innovation lies in combining high-resolution magnetic resonance diffusion imaging with a comprehensive multifactorial analysis that integrates genotype, age, sex, diet, and immunity as interacting variables. This approach enables the simultaneous examination of regional brain volume and fractional anisotropy changes across multiple risk factors, providing a more holistic view than traditional univariate analyses. Our proposed method effectively identified how these factors converge on specific brain regions \u2013 with genotype influencing the caudate putamen, pons, cingulate cortex, and cerebellum; sex affecting the amygdala and piriform cortex; and immune status impacting association cortices and cerebellar nuclei. Importantly, our integrated approach revealed factor interactions that would remain undetected in single-variable studies, particularly in the amygdala, thalamus, and pons. While many findings align with previous research, our multidimensional framework offers a methodological advancement for studying AD risk factors by modeling their combined effects rather than isolated impacts. This approach creates a template for future studies to investigate mechanisms underlying coordinated changes in brain structure through network analyses of gene expression, metabolism, and structural pathways involved in neurodegeneration.<\/jats:p>","DOI":"10.1371\/journal.pone.0327118","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T17:42:26Z","timestamp":1755020546000},"page":"e0327118","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Network biomarkers of Alzheimer\u2019s disease risk derived from joint volume and texture covariance patterns in mouse models"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6115-719X","authenticated-orcid":true,"given":"Eric W.","family":"Bridgeford","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaewon","family":"Chung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3021-9797","authenticated-orcid":true,"given":"Robert J.","family":"Anderson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Mahzarnia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9680-8386","authenticated-orcid":true,"given":"Jacques A.","family":"Stout","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hae Sol","family":"Moon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zay Yar","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua T.","family":"Vogelstein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra","family":"Badea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"key":"pone.0327118.ref001","article-title":"Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer\u2019s disease in late-onset families","author":"EH Corder","year":"1993","journal-title":"Science"},{"key":"pone.0327118.ref002","doi-asserted-by":"crossref","first-page":"109137","DOI":"10.1016\/j.envres.2020.109137","article-title":"Alzheimer disease starts in childhood in polluted Metropolitan Mexico City. A major health crisis in progress","volume":"183","author":"L Calder\u00f3n-Garcidue\u00f1as","year":"2020","journal-title":"Environ Res"},{"key":"pone.0327118.ref003","article-title":"Potential for primary prevention of Alzheimer\u2019s disease: an analysis of population-based data","author":"S Norton","year":"2014","journal-title":"Lancet Neurology"},{"key":"pone.0327118.ref004","article-title":"Projections of Alzheimer\u2019s disease in the United States and the public health impact of delaying disease onset","author":"R Brookmeyer","year":"1998","journal-title":"American Journal of Public Health"},{"key":"pone.0327118.ref005","article-title":"Alzheimer\u2019s disease","author":"C Ballard","year":"2011","journal-title":"Lancet"},{"issue":"3","key":"pone.0327118.ref006","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1385\/JMN:23:3:157","article-title":"APOE genotype effects on Alzheimer\u2019s disease onset and epidemiology","volume":"23","author":"JW Ashford","year":"2004","journal-title":"J Mol Neurosci"},{"issue":"45","key":"pone.0327118.ref007","doi-asserted-by":"crossref","first-page":"11445","DOI":"10.1523\/JNEUROSCI.1972-08.2008","article-title":"Impact of apolipoprotein E (ApoE) polymorphism on brain ApoE levels","volume":"28","author":"DR Riddell","year":"2008","journal-title":"J Neurosci"},{"issue":"12","key":"pone.0327118.ref008","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/ng.2802","article-title":"Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer\u2019s disease","volume":"45","author":"JC Lambert","year":"2013","journal-title":"Nat Genet"},{"key":"pone.0327118.ref009","doi-asserted-by":"crossref","first-page":"1064667","DOI":"10.3389\/fnagi.2022.1064667","article-title":"Progressive structural and covariance connectivity abnormalities in patients with Alzheimer\u2019s disease","volume":"14","author":"Y Xiao","year":"2023","journal-title":"Front Aging Neurosci"},{"issue":"3","key":"pone.0327118.ref010","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1002\/hbm.23081","article-title":"Differences in structural covariance brain networks between behavioral variant frontotemporal dementia and Alzheimer\u2019s disease","volume":"37","author":"A Hafkemeijer","year":"2016","journal-title":"Hum Brain Mapp"},{"issue":"1","key":"pone.0327118.ref011","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1038\/s41598-018-19290-5","article-title":"Structural covariance of gray matter volume in HIV Vertically Infected Adolescents","volume":"8","author":"J Li","year":"2018","journal-title":"Sci Rep"},{"key":"pone.0327118.ref012","doi-asserted-by":"crossref","first-page":"110278","DOI":"10.1016\/j.spl.2024.110278","article-title":"Universally consistent K-sample tests via dependence measures","volume":"216","author":"S Panda","year":"2025","journal-title":"Stat Probab Lett"},{"key":"pone.0327118.ref013","first-page":"153","article-title":"Magnetic resonance microscopy","volume":"185","author":"A Badea","year":"2013","journal-title":"Stud Health Technol Inform"},{"key":"pone.0327118.ref014","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.nicl.2018.01.007","article-title":"Repeated mild blast exposure in young adult rats results in dynamic and persistent microstructural changes in the brain","volume":"18","author":"A Badea","year":"2018","journal-title":"Neuroimage Clin"},{"issue":"15","key":"pone.0327118.ref015","doi-asserted-by":"crossref","first-page":"5644","DOI":"10.1073\/pnas.0600549103","article-title":"Apolipoprotein E4: a causative factor and therapeutic target in neuropathology, including Alzheimer\u2019s disease","volume":"103","author":"RW Mahley","year":"2006","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"37","key":"pone.0327118.ref016","doi-asserted-by":"crossref","first-page":"7408","DOI":"10.1523\/JNEUROSCI.2994-18.2019","article-title":"Differential signaling mediated by ApoE2, ApoE3, and ApoE4 in human neurons parallels Alzheimer\u2019s disease risk","volume":"39","author":"Y-WA Huang","year":"2019","journal-title":"J Neurosci"},{"issue":"5","key":"pone.0327118.ref017","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.4049\/jimmunol.1301758","article-title":"Epigenetic silencing of the human NOS2 gene: rethinking the role of nitric oxide in human macrophage inflammatory responses","volume":"192","author":"TJ Gross","year":"2014","journal-title":"J Immunol"},{"key":"pone.0327118.ref018","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/1750-1326-9-50","article-title":"The impact of human and mouse differences in NOS2 gene expression on the brain\u2019s redox and immune environment","volume":"9","author":"MD Hoos","year":"2014","journal-title":"Mol Neurodegener"},{"issue":"1","key":"pone.0327118.ref019","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s00429-023-02731-x","article-title":"Multivariate investigation of aging in mouse models expressing the Alzheimer\u2019s protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics","volume":"229","author":"HS Moon","year":"2024","journal-title":"Brain Struct Funct"},{"key":"pone.0327118.ref020","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3389\/fninf.2019.00072","article-title":"Identifying vulnerable brain networks in mouse models of genetic risk factors for late onset Alzheimer\u2019s disease","volume":"13","author":"A Badea","year":"2019","journal-title":"Front Neuroinform"},{"key":"pone.0327118.ref021","unstructured":"Tamir JI, Ong F, Cheng JY, Uecker M, Lustig M. Generalized magnetic resonance image reconstruction using the Berkeley advanced reconstruction toolbox. In: ISMRM Workshop on Data Sampling & Image Reconstruction, Sedona, AZ, 2016."},{"issue":"9","key":"pone.0327118.ref022","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.1007\/s00429-018-1750-x","article-title":"Whole mouse brain structural connectomics using magnetic resonance histology","volume":"223","author":"N Wang","year":"2018","journal-title":"Brain Struct Funct"},{"key":"pone.0327118.ref023","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.neuroimage.2014.07.061","article-title":"Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data","volume":"103","author":"B Jeurissen","year":"2014","journal-title":"Neuroimage"},{"key":"pone.0327118.ref024","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.neuroimage.2012.11.049","article-title":"SIFT: Spherical-deconvolution informed filtering of tractograms","volume":"67","author":"RE Smith","year":"2013","journal-title":"Neuroimage"},{"issue":"1","key":"pone.0327118.ref025","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1002\/ima.22005","article-title":"MRtrix: diffusion tractography in crossing fiber regions","volume":"22","author":"J Tournier","year":"2012","journal-title":"Int J Imaging Syst Tech"},{"key":"pone.0327118.ref026","unstructured":"Anderson RJ, Wang N, Cook JJ, Cofer GP, Dibb R, Johnson GA, et al. A high performance computing cluster implementation of compressed sensing reconstruction for MR histology. In: Proceedings of the International Society for Magnetic Resonance in Medicine. 2018."},{"issue":"3","key":"pone.0327118.ref027","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1007\/s12021-018-9410-0","article-title":"Small Animal Multivariate Brain Analysis (SAMBA) - a high throughput pipeline with a validation framework","volume":"17","author":"RJ Anderson","year":"2019","journal-title":"Neuroinformatics"},{"issue":"11","key":"pone.0327118.ref028","doi-asserted-by":"crossref","first-page":"4628","DOI":"10.1093\/cercor\/bhv121","article-title":"A diffusion MRI tractography connectome of the mouse brain and comparison with neuronal tracer data","volume":"25","author":"E Calabrese","year":"2015","journal-title":"Cereb Cortex"},{"key":"pone.0327118.ref029","doi-asserted-by":"crossref","first-page":"110251","DOI":"10.1016\/j.mri.2024.110251","article-title":"Brain network fingerprints of Alzheimer\u2019s disease risk factors in mouse models with humanized APOE alleles","volume":"114","author":"S Winter","year":"2024","journal-title":"Magn Reson Imaging"},{"key":"pone.0327118.ref030","unstructured":"Badea A. Mouse brain atlas ex vivo MRI. 2024. https:\/\/doi.org\/10.5281\/zenodo.10652239"},{"key":"pone.0327118.ref031","unstructured":"Bonato A, Chung FRK. Random dot product graph models for social networks. Algorithms and models for the web-graph. Berlin, Heidelberg: Springer; 2007. p. 138\u201349."},{"issue":"499","key":"pone.0327118.ref032","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1080\/01621459.2012.699795","article-title":"A consistent adjacency spectral embedding for stochastic blockmodel graphs","volume":"107","author":"DL Sussman","year":"2012","journal-title":"Journal of the American Statistical Association"},{"issue":"3","key":"pone.0327118.ref033","doi-asserted-by":"crossref","DOI":"10.3150\/15-BEJ789","article-title":"A nonparametric two-sample hypothesis testing problem for random graphs","volume":"23","author":"M Tang","year":"2017","journal-title":"Bernoulli"},{"issue":"2","key":"pone.0327118.ref034","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.csda.2005.09.010","article-title":"Automatic dimensionality selection from the scree plot via the use of profile likelihood","volume":"51","author":"M Zhu","year":"2006","journal-title":"Computational Statistics & Data Analysis"},{"key":"pone.0327118.ref035","unstructured":"Bridgeford EW, Loftus AR, Vogelstein JT. Hands-on network machine learning with python. 2025. https:\/\/www.cambridge.org\/core\/books\/handson-network-machine-learning-with-python\/9735741A096973A9C963E930BBAF5368."},{"issue":"1","key":"pone.0327118.ref036","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1146\/annurev-statistics-042720-023234","article-title":"Statistical connectomics","volume":"8","author":"J Chung","year":"2021","journal-title":"Annu Rev Stat Appl"},{"issue":"499","key":"pone.0327118.ref037","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1080\/01621459.2012.699795","article-title":"A consistent adjacency spectral embedding for stochastic blockmodel graphs","volume":"107","author":"DL Sussman","year":"2012","journal-title":"Journal of the American Statistical Association"},{"issue":"1","key":"pone.0327118.ref038","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/TPAMI.2013.135","article-title":"Consistent latent position estimation and vertex classification for random dot product graphs","volume":"36","author":"DL Sussman","year":"2014","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"pone.0327118.ref039","doi-asserted-by":"crossref","DOI":"10.1214\/14-EJS978","article-title":"Perfect clustering for stochastic blockmodel graphs via adjacency spectral embedding","volume":"8","author":"V Lyzinski","year":"2014","journal-title":"Electron J Statist"},{"issue":"226","key":"pone.0327118.ref040","first-page":"1","article-title":"Statistical inference on random dot product graphs: a survey","volume":"18","author":"A Athreya","year":"2018","journal-title":"Journal of Machine Learning Research"},{"key":"pone.0327118.ref041","doi-asserted-by":"crossref","unstructured":"Levin K, Athreya A, Tang M, Lyzinski V, Priebe CE. A central limit theorem for an omnibus embedding of multiple random dot product graphs. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW). 2017. p. 964\u20137. https:\/\/doi.org\/10.1109\/icdmw.2017.132","DOI":"10.1109\/ICDMW.2017.132"},{"key":"pone.0327118.ref042","doi-asserted-by":"crossref","unstructured":"Levin K, Athreya A, Tang M, Lyzinski V, Priebe CE. A central limit theorem for an omnibus embedding of multiple random dot product graphs. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW). 2017. p. 18\u201321.","DOI":"10.1109\/ICDMW.2017.132"},{"issue":"2","key":"pone.0327118.ref043","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.csda.2005.09.010","article-title":"Automatic dimensionality selection from the scree plot via the use of profile likelihood","volume":"51","author":"M Zhu","year":"2006","journal-title":"Computational Statistics & Data Analysis"},{"issue":"2","key":"pone.0327118.ref044","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1017\/S0080456800012163","article-title":"XV.\u2014The correlation between relatives on the supposition of mendelian inheritance.","volume":"52","author":"RA Fisher","year":"1919","journal-title":"Trans R Soc Edinb"},{"issue":"2","key":"pone.0327118.ref045","first-page":"176","article-title":"Multivariate analysis","volume":"9","author":"MS Bartlett","year":"1947","journal-title":"Supplement to the Journal of the Royal Statistical Society"},{"issue":"185","key":"pone.0327118.ref046","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1080\/01621459.1934.10502686","article-title":"The analysis of multiple classifications with unequal numbers in the different classes","volume":"29","author":"F Yates","year":"1934","journal-title":"Journal of the American Statistical Association"},{"key":"pone.0327118.ref047","doi-asserted-by":"crossref","unstructured":"Pituch KA, Stevens JP. Applied multivariate statistics for the social sciences. Taylor & Francis; 2015.","DOI":"10.4324\/9781315814919"},{"issue":"1","key":"pone.0327118.ref048","first-page":"17","article-title":"A primer on multivariate analysis of variance (MANOVA) for behavioral scientists","volume":"19","author":"R Warne","year":"2014","journal-title":"Practical Assessment, Research, and Evaluation"},{"issue":"6","key":"pone.0327118.ref049","doi-asserted-by":"crossref","DOI":"10.1214\/009053607000000505","article-title":"Measuring and testing dependence by correlation of distances","volume":"35","author":"GJ Sz\u00e9kely","year":"2007","journal-title":"Ann Statist"},{"issue":"6","key":"pone.0327118.ref050","doi-asserted-by":"crossref","DOI":"10.1214\/009053607000000505","article-title":"Measuring and testing dependence by correlation of distances","volume":"35","author":"GJ Sz\u00e9kely","year":"2007","journal-title":"Ann Statist"},{"issue":"1","key":"pone.0327118.ref051","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1080\/10618600.2021.1938585","article-title":"The chi-square test of distance correlation","volume":"31","author":"C Shen","year":"2022","journal-title":"J Comput Graph Stat"},{"key":"pone.0327118.ref052","first-page":"65","article-title":"A simple sequentially rejective multiple test procedure","author":"S Holm","year":"1979","journal-title":"Scandinavian journal of statistics"},{"issue":"1","key":"pone.0327118.ref053","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1038\/s41531-024-00647-9","article-title":"Exploiting macro- and micro-structural brain changes for improved Parkinson\u2019s disease classification from MRI data","volume":"10","author":"M Camacho","year":"2024","journal-title":"NPJ Parkinsons Dis"},{"key":"pone.0327118.ref054","doi-asserted-by":"crossref","first-page":"1183312","DOI":"10.3389\/fnins.2023.1183312","article-title":"Apolipoprotein-E deficiency leads to brain network alteration characterized by diffusion MRI and graph theory","volume":"17","author":"MC Stapleton","year":"2023","journal-title":"Front Neurosci"},{"issue":"23","key":"pone.0327118.ref055","doi-asserted-by":"crossref","first-page":"8299","DOI":"10.1073\/pnas.0500579102","article-title":"Correlations between apolipoprotein E epsilon4 gene dose and brain-imaging measurements of regional hypometabolism","volume":"102","author":"EM Reiman","year":"2005","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pone.0327118.ref056","doi-asserted-by":"crossref","first-page":"867053","DOI":"10.3389\/fncir.2022.867053","article-title":"Anatomy and function of ventral tegmental area glutamate neurons","volume":"16","author":"J Cai","year":"2022","journal-title":"Front Neural Circuits"},{"issue":"4","key":"pone.0327118.ref057","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.3233\/JAD-215372","article-title":"Associations between brainstem volume and Alzheimer\u2019s disease pathology in middle-aged individuals of the framingham heart study","volume":"86","author":"HIL Jacobs","year":"2022","journal-title":"J Alzheimers Dis"},{"key":"pone.0327118.ref058","unstructured":"Mokhtar M, Singh P. Neuroanatomy, periaqueductal gray. StatPearls. StatPearls Publishing; 2023."},{"issue":"10","key":"pone.0327118.ref059","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1038\/nn.2192","article-title":"Sparse odor representation and olfactory learning","volume":"11","author":"I Ito","year":"2008","journal-title":"Nat Neurosci"},{"issue":"6","key":"pone.0327118.ref060","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1093\/cercor\/11.6.490","article-title":"Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging","volume":"11","author":"JM Goldstein","year":"2001","journal-title":"Cereb Cortex"},{"key":"pone.0327118.ref061","doi-asserted-by":"crossref","first-page":"178","DOI":"10.3389\/fnbeh.2018.00178","article-title":"A novel role for the periaqueductal gray in consummatory behavior","volume":"12","author":"VL Tryon","year":"2018","journal-title":"Front Behav Neurosci"},{"issue":"3","key":"pone.0327118.ref062","article-title":"The lateral hypothalamic and BNST GABAergic projections to the anterior ventrolateral periaqueductal gray regulate feeding","volume":"28","author":"S Hao","year":"2019","journal-title":"Cell Rep"},{"issue":"1","key":"pone.0327118.ref063","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1111\/jdi.13708","article-title":"Regulation of food intake by intestinal hormones in brain","volume":"13","author":"N Harada","year":"2022","journal-title":"J Diabetes Investig"},{"key":"pone.0327118.ref064","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.heares.2009.01.007","article-title":"Interactions of hearing loss and diabetes mellitus in the middle age CBA\/CaJ mouse model of presbycusis","volume":"249","author":"ON Vasilyeva","year":"2009","journal-title":"Hear Res"},{"issue":"1","key":"pone.0327118.ref065","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1186\/s12974-016-0666-8","article-title":"High-fat diet feeding differentially affects the development of inflammation in the central nervous system","volume":"13","author":"O Guillemot-Legris","year":"2016","journal-title":"J Neuroinflammation"},{"key":"pone.0327118.ref066","unstructured":"Javed N, Cascella M. Neuroanatomy, globus pallidus. StatPearls. StatPearls Publishing; 2023."},{"key":"pone.0327118.ref067","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.neuroimage.2013.09.048","article-title":"Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population","volume":"84","author":"BB Avants","year":"2014","journal-title":"Neuroimage"},{"key":"pone.0327118.ref068","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.mri.2019.03.022","article-title":"Multivariate MR biomarkers better predict cognitive dysfunction in mouse models of Alzheimer\u2019s disease","volume":"60","author":"A Badea","year":"2019","journal-title":"Magn Reson Imaging"},{"issue":"9","key":"pone.0327118.ref069","doi-asserted-by":"crossref","first-page":"5307","DOI":"10.1093\/cercor\/bhac419","article-title":"Identifying vulnerable brain networks associated with Alzheimer\u2019s disease risk","volume":"33","author":"A Mahzarnia","year":"2023","journal-title":"Cereb Cortex"},{"issue":"2","key":"pone.0327118.ref070","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s43588-021-00029-8","article-title":"Similarity-driven multi-view embeddings from high-dimensional biomedical data","volume":"1","author":"BB Avants","year":"2021","journal-title":"Nat Comput Sci"},{"issue":"2","key":"pone.0327118.ref071","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.neuroimage.2011.09.069","article-title":"Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer\u2019s disease","volume":"59","author":"D Zhang","year":"2012","journal-title":"Neuroimage"},{"key":"pone.0327118.ref072","article-title":"3D inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer\u2019s disease diagnostics","author":"A Khvostikov","year":"2018","journal-title":"arXiv preprint"},{"key":"pone.0327118.ref073","doi-asserted-by":"crossref","unstructured":"Moon HS, Mahzarnia A, Stout J, Anderson RJ, Han ZY, Badea CT, et al. Predicting brain age, associated structural networks in mouse models with humanized ApoE alleles using integrative and interpretable graph neural networks. In: Medical Imaging 2024 : Computer-Aided Diagnosis. SPIE; 2024. p. 268\u201375.","DOI":"10.1117\/12.3005695"},{"key":"pone.0327118.ref074","first-page":"2023","article-title":"Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer\u2019s disease","author":"HS Moon","year":"2023","journal-title":"bioRxiv"},{"issue":"6","key":"pone.0327118.ref075","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1016\/j.neuron.2011.12.040","article-title":"A network diffusion model of disease progression in dementia","volume":"73","author":"A Raj","year":"2012","journal-title":"Neuron"},{"issue":"1","key":"pone.0327118.ref076","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neuron.2009.03.024","article-title":"Neurodegenerative diseases target large-scale human brain networks","volume":"62","author":"WW Seeley","year":"2009","journal-title":"Neuron"},{"key":"pone.0327118.ref077","first-page":"267","article-title":"Effect of high fat diet on obesity, visceral fat accumulation, and metabolic complications in ApoE knockout mice","volume":"494","author":"KT Nam","year":"2017","journal-title":"Biochem Biophys Res Commun"},{"issue":"5","key":"pone.0327118.ref078","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1007\/s00401-016-1547-z","article-title":"The role of APOE in cerebrovascular dysfunction","volume":"131","author":"LM Tai","year":"2016","journal-title":"Acta Neuropathol"},{"key":"pone.0327118.ref079","first-page":"57","article-title":"Mouse models of Alzheimer\u2019s disease","volume":"323","author":"SM Neuner","year":"2017","journal-title":"Behav Brain Res"},{"issue":"3","key":"pone.0327118.ref080","first-page":"110","article-title":"Sex hormone effects on body fluid regulation","volume":"44","author":"JL Podcasy","year":"2016","journal-title":"Exerc Sport Sci Rev"},{"issue":"158","key":"pone.0327118.ref081","first-page":"7","article-title":"GraSPy: graph statistics in python","volume":"20","author":"J Chung","year":"2019","journal-title":"J Mach Learn Res"},{"issue":"7825","key":"pone.0327118.ref082","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"CR Harris","year":"2020","journal-title":"Nature"},{"issue":"3","key":"pone.0327118.ref083","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in Python","volume":"17","author":"P Virtanen","year":"2020","journal-title":"Nat Methods"},{"issue":"9","key":"pone.0327118.ref084","first-page":"1","article-title":"Pandas: a foundational python library for data analysis and statistics","volume":"14","author":"W McKinney","year":"2011","journal-title":"Python for High Performance and Scientific Computing"},{"key":"pone.0327118.ref085","unstructured":"Hagberg A, Swart P, Schult D. Exploring network structure, dynamics, and function using networkx. Los Alamos, NM (United States): Los Alamos National Lab; 2008. https:\/\/www.osti.gov\/biblio\/960616"},{"issue":"3","key":"pone.0327118.ref086","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: a 2D graphics environment","volume":"9","author":"JD Hunter","year":"2007","journal-title":"Comput Sci Eng"},{"issue":"60","key":"pone.0327118.ref087","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"seaborn: statistical data visualization","volume":"6","author":"M Waskom","year":"2021","journal-title":"JOSS"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327118","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T17:42:36Z","timestamp":1755020556000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327118"}},"subtitle":[],"editor":[{"given":"Ioannis","family":"Liampas","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,8,12]]},"references-count":87,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8,12]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0327118","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,12]]}}}