{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T01:30:35Z","timestamp":1794533435005,"version":"4.9.1"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Research Council of Finland","doi-asserted-by":"publisher","award":["358944"],"award-info":[{"award-number":["358944"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Research Council of Finland","doi-asserted-by":"publisher","award":["346934"],"award-info":[{"award-number":["346934"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Research Council of Finland","doi-asserted-by":"publisher","award":["351849"],"award-info":[{"award-number":["351849"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003413","name":"Bundesministerium f\u00fcr Wissenschaft, Forschung und Wirtschaft","doi-asserted-by":"publisher","award":["ERAPERMED2021-127"],"award-info":[{"award-number":["ERAPERMED2021-127"]}],"id":[{"id":"10.13039\/501100003413","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013699","name":"Bundesministerium Bildung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013699","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurobiology of Aging"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.neurobiolaging.2025.06.010","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T15:43:52Z","timestamp":1752853432000},"page":"53-65","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Integrating plasma, MRI, and cognitive biomarkers for personalized prediction of decline across cognitive domains"],"prefix":"10.1016","volume":"155","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6349-0367","authenticated-orcid":false,"given":"Elaheh","family":"Moradi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Dahnke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vandad","family":"Imani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Gaser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alina","family":"Solomon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jussi","family":"Tohka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.neurobiolaging.2025.06.010_b1","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.4103\/1673-5374.367840","article-title":"Detection of Alzheimer\u2019s disease onset using MRI and PET neuroimaging: Longitudinal data analysis and machine learning","volume":"18","author":"Aberathne","year":"2023","journal-title":"Neural Regen. Res."},{"issue":"3","key":"10.1016\/j.neurobiolaging.2025.06.010_b2","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1016\/j.neuroimage.2010.12.049","article-title":"Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation","volume":"55","author":"Ashburner","year":"2011","journal-title":"Neuroimage"},{"issue":"6","key":"10.1016\/j.neurobiolaging.2025.06.010_b3","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1111\/joim.12816","article-title":"Biomarkers for Alzheimer\u2019s disease: Current status and prospects for the future","volume":"284","author":"Blennow","year":"2018","journal-title":"J. Intern. Med."},{"issue":"1","key":"10.1016\/j.neurobiolaging.2025.06.010_b4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1186\/s13195-022-01117-1","article-title":"The performance of plasma amyloid beta measurements in identifying amyloid plaques in Alzheimer\u2019s disease: A literature review","volume":"14","author":"Brand","year":"2022","journal-title":"Alzheimers Res. Ther."},{"issue":"1","key":"10.1016\/j.neurobiolaging.2025.06.010_b5","article-title":"P-Tau\/A\u03b242 and A\u03b242\/40 ratios in CSF are equally predictive of amyloid PET status","volume":"13","author":"Campbell","year":"2021","journal-title":"Alzheimers Dement. Diagn. Assess. Dis. Monit."},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.nbd.2023.106310","article-title":"Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer\u2019s dementia 3-year post MCI diagnosis","volume":"187","author":"Cao","year":"2023","journal-title":"Neurobiol. Dis."},{"issue":"1","key":"10.1016\/j.neurobiolaging.2025.06.010_b7","article-title":"Development and validation of language and visuospatial composite scores in ADNI","volume":"6","author":"Choi","year":"2020","journal-title":"Alzheimers Dement. Transl. Res. Clin. Interv."},{"issue":"4","key":"10.1016\/j.neurobiolaging.2025.06.010_b8","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1007\/s11682-012-9186-z","article-title":"Development and assessment of a composite score for memory in the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI)","volume":"6","author":"Crane","year":"2012","journal-title":"Brain Imaging Behav."},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b9","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neuroimage.2012.09.050","article-title":"Cortical thickness and central surface estimation","volume":"65","author":"Dahnke","year":"2013","journal-title":"Neuroimage"},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b10","series-title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into Gyral Based Regions of interest","first-page":"968","author":"Desikan","year":"2006"},{"issue":"4","key":"10.1016\/j.neurobiolaging.2025.06.010_b11","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0121945","article-title":"Cocor: A Comprehensive Solution for the Statistical Comparison of Correlations","volume":"10","author":"Diedenhofen","year":"2015","journal-title":"PLoS One"},{"issue":"15","key":"10.1016\/j.neurobiolaging.2025.06.010_b12","doi-asserted-by":"crossref","first-page":"e1580","DOI":"10.1212\/WNL.0000000000009240","article-title":"Total A\u03b242\/A\u03b240 ratio in plasma predicts amyloid-PET status, independent of clinical AD diagnosis","volume":"94","author":"Doecke","year":"2020","journal-title":"Neurology"},{"issue":"5","key":"10.1016\/j.neurobiolaging.2025.06.010_b13","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.jalz.2018.12.009","article-title":"Early vs. Late MCI: Improved MCI staging using a Neuropsychological Approach","volume":"15","author":"Edmonds","year":"2019","journal-title":"Alzheimers Dement."},{"issue":"1","key":"10.1016\/j.neurobiolaging.2025.06.010_b14","article-title":"Plasma P-tau181\/A\u03b21-42 ratio predicts A\u03b2-PET status and correlates with CSF-p-tau181\/A\u03b21-42 and future cognitive decline","volume":"14","author":"Fowler","year":"2022","journal-title":"Alzheimers Dement. Diagn. Assess. Dis. Monit."},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b15","series-title":"Package \u2018glmnet\u2019","author":"Friedman","year":"2021"},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b16","doi-asserted-by":"crossref","first-page":"giae049","DOI":"10.1093\/gigascience\/giae049","article-title":"CAT: A computational anatomy toolbox for the analysis of structural MRI data","volume":"13","author":"Gaser","year":"2024","journal-title":"Gigascience"},{"issue":"4","key":"10.1016\/j.neurobiolaging.2025.06.010_b17","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s11682-012-9176-1","article-title":"A composite score for executive functioning, validated in Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment","volume":"6","author":"Gibbons","year":"2012","journal-title":"Brain Imaging Behav."},{"issue":"18","key":"10.1016\/j.neurobiolaging.2025.06.010_b18","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1093\/bioinformatics\/btw313","article-title":"Complex heatmaps reveal patterns and correlations in multidimensional genomic data","volume":"32","author":"Gu","year":"2016","journal-title":"Bioinformatics"},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b19","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit","first-page":"5207","article-title":"A Multimodal Approach Integrating Convolutional and Recurrent Neural Networks for Alzheimer\u2019s Disease Temporal Progression Prediction","author":"Hl","year":"2024"},{"issue":"4","key":"10.1016\/j.neurobiolaging.2025.06.010_b20","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA Research Framework: Toward a biological definition of Alzheimer\u2019s disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimers Dement."},{"issue":"11","key":"10.1016\/j.neurobiolaging.2025.06.010_b21","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0277322","article-title":"Machine learning based multi-modal prediction of future decline toward Alzheimer\u2019s disease: An empirical study","volume":"17","author":"Karaman","year":"2022","journal-title":"PLoS One"},{"issue":"5","key":"10.1016\/j.neurobiolaging.2025.06.010_b22","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/S1474-4422(20)30071-5","article-title":"Blood phosphorylated tau 181 as a biomarker for Alzheimer\u2019s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts","volume":"19","author":"Karikari","year":"2020","journal-title":"Lancet Neurol."},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R using the caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b24","series-title":"The Alzheimer\u2019s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up","author":"Marinescu","year":"2020"},{"issue":"4","key":"10.1016\/j.neurobiolaging.2025.06.010_b25","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1001\/jamaneurol.2022.5272","article-title":"Prediction of Longitudinal Cognitive Decline in Preclinical Alzheimer Disease Using Plasma Biomarkers","volume":"80","author":"Mattsson-Carlgren","year":"2023","journal-title":"JAMA Neurol."},{"issue":"3","key":"10.1016\/j.neurobiolaging.2025.06.010_b26","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.jalz.2011.03.005","article-title":"The diagnosis of dementia due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"McKhann","year":"2011","journal-title":"Alzheimers Dement."},{"issue":"1","key":"10.1016\/j.neurobiolaging.2025.06.010_b27","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1186\/s13195-024-01478-9","article-title":"Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients","volume":"16","author":"Mendes","year":"2024","journal-title":"Alzheimers Res. Ther."},{"issue":"18","key":"10.1016\/j.neurobiolaging.2025.06.010_b28","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1056\/NEJMoa2100708","article-title":"Donanemab in Early Alzheimer\u2019s Disease","volume":"384","author":"Mintun","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b29","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.neurobiolaging.2019.07.004","article-title":"Supervised pathway analysis of blood gene expression profiles in Alzheimer\u2019s disease","volume":"84","author":"Moradi","year":"2019","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b30","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.neuroimage.2014.10.002","article-title":"Machine learning framework for early MRI-based Alzheimer\u2019s conversion prediction in MCI subjects","volume":"104","author":"Moradi","year":"2015","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.neurobiolaging.2025.06.010_b31","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s13195-024-01415-w","article-title":"Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals","volume":"16","author":"Moradi","year":"2024","journal-title":"Alzheimers Res. Ther."},{"issue":"8","key":"10.1016\/j.neurobiolaging.2025.06.010_b32","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.jalz.2017.06.2266","article-title":"Amyloid \u03b2 concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis","volume":"13","author":"Ovod","year":"2017","journal-title":"Alzheimers Dement."},{"issue":"9","key":"10.1016\/j.neurobiolaging.2025.06.010_b33","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1001\/jamaneurol.2019.1632","article-title":"Performance of Fully Automated Plasma Assays as Screening Tests for Alzheimer Disease\u2013Related \u03b2-Amyloid Status","volume":"76","author":"Palmqvist","year":"2019","journal-title":"JAMA Neurol."},{"issue":"3","key":"10.1016\/j.neurobiolaging.2025.06.010_b34","first-page":"718","article-title":"Package \u2018daim\u2019","volume":"53","author":"Potapov","year":"2009","journal-title":"Comput. Statist. Data Anal."},{"issue":"1","key":"10.1016\/j.neurobiolaging.2025.06.010_b35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/1471-2105-12-77","article-title":"pROC: An open-source package for R and S+ to analyze and compare ROC curves","volume":"12","author":"Robin","year":"2011","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"10.1016\/j.neurobiolaging.2025.06.010_b36","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1214\/10-STS330","article-title":"To Explain or to Predict?","volume":"25","author":"Shmueli","year":"2010","journal-title":"Stat. Sci."},{"issue":"4","key":"10.1016\/j.neurobiolaging.2025.06.010_b37","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s00401-022-02408-5","article-title":"Plasma biomarkers for Alzheimer\u2019s Disease in relation to neuropathology and cognitive change","volume":"143","author":"Smirnov","year":"2022","journal-title":"Acta Neuropathol."},{"issue":"8","key":"10.1016\/j.neurobiolaging.2025.06.010_b38","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1038\/nrneurol.2015.119","article-title":"Clearance systems in the brain\u2014Implications for Alzheimer disease","volume":"11","author":"Tarasoff-Conway","year":"2015","journal-title":"Nat. Rev. Neurol."},{"issue":"10","key":"10.1016\/j.neurobiolaging.2025.06.010_b39","first-page":"28","article-title":"Package \u2018survival\u2019","volume":"128","author":"Therneau","year":"2015","journal-title":"R. Top Doc."},{"issue":"1","key":"10.1016\/j.neurobiolaging.2025.06.010_b40","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1056\/NEJMoa2212948","article-title":"Lecanemab in Early Alzheimer\u2019s Disease","volume":"388","author":"Van Dyck","year":"2023","journal-title":"N. Engl. J. Med."},{"issue":"9","key":"10.1016\/j.neurobiolaging.2025.06.010_b41","doi-asserted-by":"crossref","first-page":"4226","DOI":"10.1002\/alz.13116","article-title":"Translating blood biomarkers into clinical practice for Alzheimer\u2019s disease: Challenges and perspectives","volume":"19","author":"Wang","year":"2023","journal-title":"Alzheimers Dement."},{"issue":"1","key":"10.1016\/j.neurobiolaging.2025.06.010_b42","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s13024-021-00451-6","article-title":"A blood-based diagnostic test incorporating plasma A\u03b242\/40 ratio, ApoE proteotype, and age accurately identifies brain amyloid status: Findings from a multi cohort validity analysis","volume":"16","author":"West","year":"2021","journal-title":"Mol. Neurodegener."},{"issue":"2","key":"10.1016\/j.neurobiolaging.2025.06.010_b43","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1002\/wics.147","article-title":"Ggplot2","volume":"3","author":"Wickham","year":"2011","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"issue":"2","key":"10.1016\/j.neurobiolaging.2025.06.010_b44","doi-asserted-by":"crossref","first-page":"e134","DOI":"10.1111\/j.1552-6569.2010.00484.x","article-title":"Algorithms to Improve the Reparameterization of Spherical Mappings of Brain Surface Meshes","volume":"21","author":"Yotter","year":"2011","journal-title":"J. Neuroimaging"},{"key":"10.1016\/j.neurobiolaging.2025.06.010_b45","doi-asserted-by":"crossref","first-page":"0354","DOI":"10.34133\/research.0354","article-title":"Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer\u2019s Disease Conversion: Evidence from a 2-Cohort Longitudinal Study","volume":"7","author":"Yu","year":"2024","journal-title":"Research"},{"issue":"6","key":"10.1016\/j.neurobiolaging.2025.06.010_b46","doi-asserted-by":"crossref","first-page":"4199","DOI":"10.1002\/alz.13829","article-title":"Association of plasma biomarkers of Alzheimer\u2019s disease and related disorders with cognition and cognitive decline: The MYHAT population-based study","volume":"20","author":"Zhang","year":"2024","journal-title":"Alzheimers Dement."}],"container-title":["Neurobiology of Aging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0197458025001137?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0197458025001137?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:20:01Z","timestamp":1777105201000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0197458025001137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":46,"alternative-id":["S0197458025001137"],"URL":"https:\/\/doi.org\/10.1016\/j.neurobiolaging.2025.06.010","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.12.31.24319830","asserted-by":"object"}]},"ISSN":["0197-4580"],"issn-type":[{"value":"0197-4580","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Integrating plasma, MRI, and cognitive biomarkers for personalized prediction of decline across cognitive domains","name":"articletitle","label":"Article Title"},{"value":"Neurobiology of Aging","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neurobiolaging.2025.06.010","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}