{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:20:28Z","timestamp":1783124428404,"version":"3.54.6"},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U19 AG024904"],"award-info":[{"award-number":["U19 AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011011","name":"Junta de Andalucia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011011","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Spain Ministry of Science Innovation and Universities","doi-asserted-by":"publisher","award":["PID2022-137629OA-I00"],"award-info":[{"award-number":["PID2022-137629OA-I00"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Spain Ministry of Science Innovation and Universities","doi-asserted-by":"publisher","award":["PID2022- 137461NB-C32"],"award-info":[{"award-number":["PID2022- 137461NB-C32"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014440","name":"Spain Ministry of Science Innovation and Universities","doi-asserted-by":"publisher","award":["PID2022-137451OB-I00"],"award-info":[{"award-number":["PID2022-137451OB-I00"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009804","name":"Northern California Institute for Research and Education","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014041","name":"Alzheimer&apos;s Disease Neuroimaging Initiative","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014041","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":["NeuroImage"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neuroimage.2026.121917","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:00:56Z","timestamp":1776841256000},"page":"121917","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Robust validation of neuroimaging and clinical models via the SAR method: A case study based on the ADNI dataset"],"prefix":"10.1016","volume":"333","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1672-3457","authenticated-orcid":false,"given":"A.","family":"Hern\u00e1ndez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5094-8911","authenticated-orcid":false,"given":"I.A.","family":"Ill\u00e1n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Ram\u00edrez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1940-8834","authenticated-orcid":false,"given":"F.","family":"Segovia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F.J.","family":"Mart\u00ednez-Murcia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Levin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7069-1714","authenticated-orcid":false,"given":"J.M.","family":"G\u00f3rriz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"7","key":"10.1016\/j.neuroimage.2026.121917_b1","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1049\/el.2009.3415","article-title":"Alzheimer\u2019s diagnosis using eigenbrains and support vector machines","volume":"45","author":"\u00c1lvarez","year":"2009","journal-title":"Electron. Lett."},{"issue":"2","key":"10.1016\/j.neuroimage.2026.121917_b2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1001\/archneur.1994.00540140051014","article-title":"Neuropathologic changes of the temporal pole in Alzheimer\u2019s disease and pick\u2019s disease","volume":"51","author":"Arnold","year":"1994","journal-title":"Arch. Neurol."},{"issue":"1","key":"10.1016\/j.neuroimage.2026.121917_b3","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","article-title":"A fast diffeomorphic image registration algorithm","volume":"38","author":"Ashburner","year":"2007","journal-title":"NeuroImage"},{"issue":"6","key":"10.1016\/j.neuroimage.2026.121917_b4","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","article-title":"Voxel-based morphometry\u2013the methods","volume":"11","author":"Ashburner","year":"2000","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.neuroimage.2026.121917_b5","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","article-title":"Unified segmentation","volume":"26","author":"Ashburner","year":"2005","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2026.121917_b6","article-title":"ICAM-Reg: Interpretable classification and regression with feature attribution for mapping neurological phenotypes in individual scans","volume":"PP","author":"Bass","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2026.121917_b7","first-page":"1089","article-title":"No unbiased estimator of the variance of K-Fold cross-validation","volume":"5","author":"Bengio","year":"2004","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"10.1016\/j.neuroimage.2026.121917_b8","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1503\/jpn.180016","article-title":"An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures","volume":"44","author":"Bhagwat","year":"2019","journal-title":"J. Psychiatry Neurosci."},{"issue":"3","key":"10.1016\/j.neuroimage.2026.121917_b9","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1093\/bioinformatics\/btg419","article-title":"Is cross-validation valid for small-sample microarray classification?","volume":"20","author":"Braga-Neto","year":"2004","journal-title":"Bioinformatics"},{"issue":"99","key":"10.1016\/j.neuroimage.2026.121917_b10","article-title":"Joint Multi-Modal longitudinal regression and classification for Alzheimer\u2019s disease prediction","volume":"PP","author":"Brand","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2026.121917_b11","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1037\/a0016161","article-title":"The prevalence of cortical gray matter atrophy may be overestimated in the healthy aging brain","volume":"23","author":"Burgmans","year":"2009","journal-title":"Neuropsychology"},{"key":"10.1016\/j.neuroimage.2026.121917_b12","first-page":"30","article-title":"Reproducible evaluation of methods for predicting progression to Alzheimer\u2019s disease from clinical and neuroimaging data","author":"Burgos","year":"2019","journal-title":"Image Process."},{"issue":"3","key":"10.1016\/j.neuroimage.2026.121917_b13","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s12021-013-9180-7","article-title":"Semi-Supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers","volume":"11","author":"Cheng","year":"2013","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.neuroimage.2026.121917_b14","series-title":"Advances in Neural Information Processing Systems 9","first-page":"155","article-title":"Support vector regression machines","author":"Drucker","year":"1997"},{"issue":"28","key":"10.1016\/j.neuroimage.2026.121917_b15","doi-asserted-by":"crossref","first-page":"7900","DOI":"10.1073\/pnas.1602413113","article-title":"Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates","volume":"113","author":"Eklund","year":"2016","journal-title":"Proc. Natl. Acad. Sci. (PNAS)"},{"key":"10.1016\/j.neuroimage.2026.121917_b16","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.jalz.2009.04.1173","article-title":"Estimating clinical variables from brain images using Bayesian regression","volume":"5","author":"Fan","year":"2009","journal-title":"Alzheimer\u2019s Dement."},{"issue":"6","key":"10.1016\/j.neuroimage.2026.121917_b17","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1093\/brain\/119.6.2001","article-title":"Presymptomatic hippocampal atrophy in Alzheimer\u2019s disease. a longitudinal MRI study","volume":"119","author":"Fox","year":"1996","journal-title":"Brain: J. Neurol."},{"key":"10.1016\/j.neuroimage.2026.121917_b18","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.neuroimage.2013.02.057","article-title":"Sample size and the fallacies of classical inference","volume":"81","author":"Friston","year":"2013","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2026.121917_b19","series-title":"Alzheimer\u2019s disease brain MRI classification: Challenges and insights","author":"Fung","year":"2019"},{"key":"10.1016\/j.neuroimage.2026.121917_b20","doi-asserted-by":"crossref","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"},{"key":"10.1016\/j.neuroimage.2026.121917_b21","series-title":"Is K-fold validation the best model selection method for Machine Learning?","author":"Gorriz","year":"2024"},{"key":"10.1016\/j.neuroimage.2026.121917_b22","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.inffus.2020.09.008","article-title":"Statistical agnostic mapping: A framework in neuroimaging based on concentration inequalities","volume":"66","author":"Gorriz","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.neuroimage.2026.121917_b23","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2020.05.078","article-title":"Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications","volume":"410","author":"G\u00f3rriz","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neuroimage.2026.121917_b24","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.jare.2025.04.026","article-title":"Statistical agnostic regression: A machine learning method to validate regression models","volume":"80","author":"Gorriz","year":"2025","journal-title":"J. Adv. Res."},{"key":"10.1016\/j.neuroimage.2026.121917_b25","series-title":"Mathematical Aspects of Deep Learning","isbn-type":"print","doi-asserted-by":"crossref","DOI":"10.1017\/9781009025096","author":"Grohs","year":"2022","ISBN":"https:\/\/id.crossref.org\/isbn\/9781009025096"},{"issue":"2","key":"10.1016\/j.neuroimage.2026.121917_b26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/13803395.2022.2082386","article-title":"Assessing and validating reliable change across ADNI protocols","volume":"44","author":"Hammers","year":"2022","journal-title":"J. Clin. Exp. Neuropsychol."},{"issue":"3","key":"10.1016\/j.neuroimage.2026.121917_b27","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1093\/cercor\/10.3.243","article-title":"Orbitofrontal cortex pathology in Alzheimer\u2019s disease","volume":"10","author":"Hoesen","year":"2000","journal-title":"Cerebral Cortex"},{"issue":"14","key":"10.1016\/j.neuroimage.2026.121917_b28","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1016\/j.patrec.2008.06.018","article-title":"Cross-validation and bootstrapping are unreliable in small sample classification","volume":"29","author":"Isaksson","year":"2008","journal-title":"Pattern Recognit. Lett."},{"issue":"6","key":"10.1016\/j.neuroimage.2026.121917_b29","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1016\/j.neuron.2013.12.003","article-title":"Biomarker modeling of Alzheimer\u2019s disease","volume":"80","author":"Jack","year":"2013","journal-title":"Neuron"},{"issue":"8","key":"10.1016\/j.neuroimage.2026.121917_b30","doi-asserted-by":"crossref","first-page":"5143","DOI":"10.1002\/alz.13859","article-title":"Revised criteria for diagnosis and staging of Alzheimer\u2019s disease: Alzheimer\u2019s Association Workgroup","volume":"20","author":"Jack","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"10.1016\/j.neuroimage.2026.121917_b31","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.inffus.2022.11.007","article-title":"A non-parametric statistical inference framework for deep learning in current neuroimaging","volume":"91","author":"Jimenez-Mesa","year":"2023","journal-title":"Inf. Fusion"},{"issue":"16","key":"10.1016\/j.neuroimage.2026.121917_b32","article-title":"Lateralization disruption and dynamic balance alterations in Alzheimer\u2019s disease: Impacts on hemispheric interaction and cognitive performance","volume":"46","author":"Juan","year":"2025","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2026.121917_b33","series-title":"Proceedings Winter Simulation Conference","first-page":"352","article-title":"Validation of trace-driven simulation models: regression analysis revisited","author":"Kleijnen","year":"1996"},{"key":"10.1016\/j.neuroimage.2026.121917_b34","first-page":"1137","article-title":"A study of cross-validation and bootstrap for accuracy estimation and model selection","author":"Kohavi","year":"1995","journal-title":"Int. Jt. Conf. Artif. Intell. (IJCAI)"},{"key":"10.1016\/j.neuroimage.2026.121917_b35","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.nicl.2014.08.023","article-title":"Random forest ensembles for detection and prediction of Alzheimer\u2019s disease with a good between-cohort robustness","volume":"6","author":"Lebedev","year":"2014","journal-title":"NeuroImage: Clin."},{"issue":"521","key":"10.1016\/j.neuroimage.2026.121917_b36","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"5","key":"10.1016\/j.neuroimage.2026.121917_b37","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1109\/JBHI.2015.2429556","article-title":"A robust deep model for improved classification of AD\/MCI patients","volume":"19","author":"Li","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"S5","key":"10.1016\/j.neuroimage.2026.121917_b38","doi-asserted-by":"crossref","DOI":"10.1002\/alz.041799","article-title":"Machine learning regression analysis predicts brain amyloid-beta burden: A multimodal neuroimaging research study","volume":"16","author":"Lin","year":"2020","journal-title":"Alzheimer\u2019s Dement."},{"key":"10.1016\/j.neuroimage.2026.121917_b39","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2018.00003","article-title":"Changes in brain lateralization in patients with mild cognitive impairment and Alzheimer\u2019s disease: A resting-state functional magnetic resonance study from Alzheimer\u2019s disease neuroimaging initiative","volume":"9","author":"Liu","year":"2018","journal-title":"Front. Neurol."},{"issue":"3","key":"10.1016\/j.neuroimage.2026.121917_b40","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neulet.2009.08.061","article-title":"SVM-based CAD system for early detection of the Alzheimer\u2019s disease using kernel PCA and LDA","volume":"464","author":"L\u00f3pez","year":"2009","journal-title":"Neurosci. Lett."},{"key":"10.1016\/j.neuroimage.2026.121917_b41","series-title":"A PAC-Bayesian tutorial with a dropout bound","author":"McAllester","year":"2013"},{"key":"10.1016\/j.neuroimage.2026.121917_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2020.117460","article-title":"Robust parametric modeling of Alzheimer\u2019s disease progression","volume":"225","author":"Mehdipour Ghazi","year":"2021","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2026.121917_b43","doi-asserted-by":"crossref","DOI":"10.1038\/d41586-019-02960-3","article-title":"Highlight negative results to improve science","author":"Mehta","year":"2019","journal-title":"Nat. (Nat. Careers)"},{"issue":"8","key":"10.1016\/j.neuroimage.2026.121917_b44","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1002\/sim.4780100805","article-title":"Validation techniques for logistic regression models","volume":"10","author":"Miller","year":"1991","journal-title":"Stat. Med."},{"key":"10.1016\/j.neuroimage.2026.121917_b45","series-title":"The Practice of Business Statistics Companion Chapter 18: Bootstrap Methods and Permutation Tests","author":"Moore","year":"2003"},{"key":"10.1016\/j.neuroimage.2026.121917_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2023.119947","article-title":"Brain-age prediction: A systematic comparison of machine learning workflows","volume":"270","author":"More","year":"2023","journal-title":"NeuroImage"},{"issue":"1\u20132","key":"10.1016\/j.neuroimage.2026.121917_b47","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0166-4328(95)00227-8","article-title":"The primate temporal pole: its putative role in object recognition and memory","volume":"77","author":"Nakamura","year":"1996","journal-title":"Behav. Brain Res."},{"key":"10.1016\/j.neuroimage.2026.121917_b48","article-title":"On validating regression models with bootstraps and data splitting techniques","volume":"11","author":"Oredein","year":"2011","journal-title":"Glob. J. Sci. Front. Res."},{"key":"10.1016\/j.neuroimage.2026.121917_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2020.116938","article-title":"Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression","volume":"219","author":"Palma","year":"2020","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.neuroimage.2026.121917_b50","article-title":"Association of CSF biomarkers with MRI brain changes in Alzheimer\u2019s disease","volume":"16","author":"Seidu","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"issue":"4","key":"10.1016\/j.neuroimage.2026.121917_b51","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/00401706.1977.10489581","article-title":"Validation of regression models: Methods and examples","volume":"19","author":"Snee","year":"1977","journal-title":"Technometrics"},{"issue":"4","key":"10.1016\/j.neuroimage.2026.121917_b52","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1016\/j.neuroimage.2010.03.051","article-title":"Predicting clinical scores from magnetic resonance scans in Alzheimer\u2019s disease","volume":"51","author":"Stonnington","year":"2010","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2026.121917_b53","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.neurobiolaging.2015.10.015","article-title":"Gray matter network disruptions and amyloid beta in cognitively normal adults","volume":"37","author":"Tijms","year":"2016","journal-title":"Neurobiol. Aging"},{"issue":"1","key":"10.1016\/j.neuroimage.2026.121917_b54","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1162\/jocn.1991.3.1.71","article-title":"Eigenfaces for recognition","volume":"3","author":"Turk","year":"1991","journal-title":"J. Cogn. Neurosci."},{"key":"10.1016\/j.neuroimage.2026.121917_b55","series-title":"The Nature of Statistical Learning Theory","author":"Vapnik","year":"1995"},{"key":"10.1016\/j.neuroimage.2026.121917_b56","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.neuroimage.2018.03.007","article-title":"Evaluation of non-negative matrix factorization of grey matter in age prediction","volume":"173","author":"Varikuti","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2026.121917_b57","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neuroimage.2017.06.061","article-title":"Cross-validation failure: Small sample sizes lead to large error bars","volume":"180","author":"Varoquaux","year":"2018","journal-title":"NeuroImage"},{"issue":"6","key":"10.1016\/j.neuroimage.2026.121917_b58","doi-asserted-by":"crossref","first-page":"7799","DOI":"10.1109\/TPAMI.2022.3220744","article-title":"The shape of learning curves: A review","volume":"45","author":"Viering","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"10.1016\/j.neuroimage.2026.121917_b59","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1016\/j.neurobiolaging.2005.05.020","article-title":"Effects of age on volumes of cortex, white matter and subcortical structures","volume":"26","author":"Walhovd","year":"2005","journal-title":"Neurobiol. Aging"},{"issue":"5","key":"10.1016\/j.neuroimage.2026.121917_b60","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.neurobiolaging.2009.05.013","article-title":"Consistent neuroanatomical age-related volume differences across multiple samples","volume":"32","author":"Walhovd","year":"2011","journal-title":"Neurobiol. Aging"},{"issue":"4","key":"10.1016\/j.neuroimage.2026.121917_b61","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.3233\/JAD-170810","article-title":"Assay of plasma phosphorylated tau protein (threonine 181) and total tau protein in Early-Stage Alzheimer\u2019s disease","volume":"61","author":"Yang","year":"2018","journal-title":"J. Alzheimer\u2019s Dis."},{"issue":"2","key":"10.1016\/j.neuroimage.2026.121917_b62","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.neuroimage.2011.09.069","article-title":"Multi-Modal MultiTask learning for joint prediction of multiple regression and classification variables in Alzheimer \u2019 s disease","volume":"59","author":"Zhang","year":"2012","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2026.121917_b63","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neuroimage.2013.03.073","article-title":"Modeling disease progression via multi-task learning","volume":"78","author":"Zhou","year":"2013","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2026.121917_b64","series-title":"Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"814","article-title":"A multi-task learning formulation for predicting disease progression","author":"Zhou","year":"2011"}],"container-title":["NeuroImage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811926002326?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811926002326?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:49:01Z","timestamp":1783122541000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1053811926002326"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":64,"alternative-id":["S1053811926002326"],"URL":"https:\/\/doi.org\/10.1016\/j.neuroimage.2026.121917","relation":{},"ISSN":["1053-8119"],"issn-type":[{"value":"1053-8119","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Robust validation of neuroimaging and clinical models via the SAR method: A case study based on the ADNI dataset","name":"articletitle","label":"Article Title"},{"value":"NeuroImage","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neuroimage.2026.121917","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"121917"}}