{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T05:23:43Z","timestamp":1789190623791,"version":"build-2803163510"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T00:00:00Z","timestamp":1744070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000024","name":"CIHR","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003151","name":"Fonds de recherche du Qu\u00e9bec \u2013 Nature et technologies","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003151","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"name":"HBHL"},{"DOI":"10.13039\/100012498","name":"Alzheimer Society Research Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012498","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["GeroScience"],"DOI":"10.1007\/s11357-025-01645-2","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T21:47:02Z","timestamp":1744148822000},"page":"5003-5023","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploring the power of MRI and clinical measures in predicting AD neuropathology"],"prefix":"10.1007","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5197-3062","authenticated-orcid":false,"given":"Farooq","family":"Kamal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cassandra","family":"Morrison","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael D.","family":"Oliver","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahsa","family":"Dadar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,8]]},"reference":[{"key":"1645_CR1","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","volume":"14","author":"CR Jack","year":"2018","unstructured":"Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer\u2019s disease. Alzheimer\u2019s Dement. 2018;14:535\u201362. https:\/\/doi.org\/10.1016\/j.jalz.2018.02.018.","journal-title":"Alzheimer\u2019s Dement"},{"key":"1645_CR2","doi-asserted-by":"publisher","first-page":"987","DOI":"10.3233\/JAD-150400","volume":"48","author":"M Bilello","year":"2015","unstructured":"Bilello M, Doshi J, Nabavizadeh SA, Toledo JB, Erus G, Xie SX, et al. Correlating cognitive decline with white matter lesion and brain atrophy magnetic resonance imaging measurements in Alzheimer\u2019s disease. J Alzheimer\u2019s Dis. 2015;48:987\u201394. https:\/\/doi.org\/10.3233\/JAD-150400.","journal-title":"J Alzheimer\u2019s Dis"},{"key":"1645_CR3","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neurobiolaging.2021.11.007","volume":"111","author":"M Dadar","year":"2022","unstructured":"Dadar M, Manera AL, Ducharme S, Collins DL. White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer\u2019s disease and frontotemporal dementia. Neurobiol Aging. 2022;111:54\u201363. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2021.11.007.","journal-title":"Neurobiol Aging"},{"key":"1645_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11357-022-00665-6","volume":"45","author":"F Kamal","year":"2023","unstructured":"Kamal F, Morrison C, Maranzano J, Zeighami Y, Dadar M. Topographical differences in white matter hyperintensity burden and cognition in aging, MCI, and AD. GeroScience. 2023;45:1\u201316. https:\/\/doi.org\/10.1007\/s11357-022-00665-6.","journal-title":"GeroScience"},{"key":"1645_CR5","doi-asserted-by":"publisher","first-page":"103096","DOI":"10.1016\/j.nicl.2022.103096","volume":"35","author":"C Morrison","year":"2022","unstructured":"Morrison C, Dadar M, Villeneuve S, Collins DL. White matter lesions may be an early marker for age-related cognitive decline. NeuroImage Clin. 2022;35:103096. https:\/\/doi.org\/10.1016\/j.nicl.2022.103096.","journal-title":"NeuroImage Clin"},{"key":"1645_CR6","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.tins.2022.11.006","volume":"46","author":"KM Igarashi","year":"2023","unstructured":"Igarashi KM. Entorhinal cortex dysfunction in Alzheimer\u2019s disease. Trends Neurosci. 2023;46:124\u201336. https:\/\/doi.org\/10.1016\/j.tins.2022.11.006.","journal-title":"Trends Neurosci"},{"key":"1645_CR7","doi-asserted-by":"publisher","unstructured":"Fjell AM, McEvoy L, Holland D, Dale AM, Walhovd KB, Initiative. & ADN. What is normal in normal aging? Effects of Aging, Amyloid and Alzheimer\u2019s Disease on the Cerebral Cortex and the Hippocampus. Prog Neurobiol 2014;117:20\u201340. https:\/\/doi.org\/10.1016\/j.pneurobio.2014.02.004.What.","DOI":"10.1016\/j.pneurobio.2014.02.004.What"},{"key":"1645_CR8","doi-asserted-by":"publisher","first-page":"347","DOI":"10.2174\/156720509788929273","volume":"6","author":"SL Risacher","year":"2009","unstructured":"Risacher SL, Saykin AJ, West JD, Shen L, Firpi HA, Mcdonald BC, et al. Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res. 2009;6:347\u201361.","journal-title":"Curr Alzheimer Res"},{"key":"1645_CR9","doi-asserted-by":"publisher","first-page":"101911","DOI":"10.1016\/j.arr.2023.101911","volume":"87","author":"Y Chen","year":"2023","unstructured":"Chen Y, Wang Y, Song Z, Fan Y, Gao T, Tang X. Abnormal white matter changes in Alzheimer\u2019s disease based on diffusion tensor imaging: A systematic review. Ageing Res Rev. 2023;87:101911. https:\/\/doi.org\/10.1016\/j.arr.2023.101911.","journal-title":"Ageing Res Rev"},{"key":"1645_CR10","doi-asserted-by":"publisher","first-page":"515","DOI":"10.3233\/JAD-170573","volume":"63","author":"E Van Den Berg","year":"2018","unstructured":"Van Den Berg E, Geerlings MI, Biessels GJ, Nederkoorn PJ, Kloppenborg RP. White Matter Hyperintensities and Cognition in Mild Cognitive Impairment and Alzheimer\u2019s Disease: A Domain-Specific Meta-Analysis. J Alzheimer\u2019s Dis. 2018;63:515\u201327. https:\/\/doi.org\/10.3233\/JAD-170573.","journal-title":"J Alzheimer\u2019s Dis"},{"key":"1645_CR11","doi-asserted-by":"publisher","first-page":"3286","DOI":"10.1093\/brain\/awn265","volume":"131","author":"AA Gouw","year":"2008","unstructured":"Gouw AA, Seewann A, Vrenken H, Van Der Flier WM, Rozemuller JM, Barkhof F, et al. Heterogeneity of white matter hyperintensities in Alzheimer\u2019s disease: Post-mortem quantitative MRI and neuropathology. Brain. 2008;131:3286\u201398. https:\/\/doi.org\/10.1093\/brain\/awn265.","journal-title":"Brain"},{"key":"1645_CR12","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1016\/j.neurobiolaging.2007.10.015","volume":"30","author":"O Piguet","year":"2009","unstructured":"Piguet O, Double KL, Kril JJ, Harasty J, Macdonald V, McRitchie DA, et al. White matter loss in healthy ageing: A postmortem analysis. Neurobiol Aging. 2009;30:1288\u201395. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2007.10.015.","journal-title":"Neurobiol Aging"},{"key":"1645_CR13","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.jalz.2013.12.025","volume":"11","author":"KJ Bangen","year":"2015","unstructured":"Bangen KJ, Nation DA, Delano-Wood L, Weissberger GH, Hansen LA, Galasko DR, et al. Aggregate effects of vascular risk factors on cerebrovascular changes in autopsy-confirmed Alzheimer\u2019s disease. Alzheimer\u2019s Dement. 2015;11:394-403.e1. https:\/\/doi.org\/10.1016\/j.jalz.2013.12.025.","journal-title":"Alzheimer\u2019s Dement"},{"key":"1645_CR14","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1001\/archneur.56.10.1254","volume":"56","author":"PL Di Patre","year":"1999","unstructured":"Di Patre PL, Read SL, Cummings JL, Tomiyasu U, Vartavarian LM, Secor DL, Vinters HV. Progression of clinical deterioration and pathological changes in patients with Alzheimer disease evaluated at biopsy and autopsy. Arch Neurol. 1999;56:1254\u201361.","journal-title":"Arch Neurol"},{"key":"1645_CR15","doi-asserted-by":"publisher","first-page":"116077","DOI":"10.1016\/j.neuroimage.2019.116077","volume":"202","author":"E Iordanishvili","year":"2019","unstructured":"Iordanishvili E, Schall M, Lou\u00e7\u00e3o R, Zimmermann M, Kotetishvili K, Shah NJ, Oros-Peusquens AM. Quantitative MRI of cerebral white matter hyperintensities: a new approach towards understanding the underlying pathology. Neuroimage. 2019;202:116077.","journal-title":"Neuroimage"},{"key":"1645_CR16","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1007\/s00330-016-4633-7","volume":"27","author":"Y Nishiyama","year":"2017","unstructured":"Nishiyama Y, Kanayama H, Mori H, Tada K, Yamamoto Y, Katsube T, et al. Whole brain analysis of postmortem density changes of grey and white matter on computed tomography by statistical parametric mapping. Eur Radiol. 2017;27:2317\u201325.","journal-title":"Eur Radiol"},{"key":"1645_CR17","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1097\/WAD.0000000000000032","volume":"28","author":"B Kaur","year":"2014","unstructured":"Kaur B, Himali JJ, Seshadri S, Beiser AS, Au R, McKee AC, et al. Association between neuropathology and brain volume in the framingham heart study. Alzheimer Dis Assoc Disord. 2014;28:219\u201325. https:\/\/doi.org\/10.1097\/WAD.0000000000000032.","journal-title":"Alzheimer Dis Assoc Disord"},{"key":"1645_CR18","doi-asserted-by":"publisher","first-page":"5036","DOI":"10.1002\/alz.13087","volume":"19","author":"M Zhang","year":"2023","unstructured":"Zhang M, Ganz AB, Rohde S, Lorenz L, Rozemuller AJM, van Vliet K, et al. The correlation between neuropathology levels and cognitive performance in centenarians. Alzheimer\u2019s Dement. 2023;19:5036\u201347. https:\/\/doi.org\/10.1002\/alz.13087.","journal-title":"Alzheimer\u2019s Dement"},{"key":"1645_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnagi.2013.00050","volume":"5","author":"PA Boyle","year":"2013","unstructured":"Boyle PA, Yu L, Wilson RS, Schneider JA, Bennett DA. Relation of neuropathology with cognitive decline among older persons without dementia. Front Aging Neurosci. 2013;5:1\u20138. https:\/\/doi.org\/10.3389\/fnagi.2013.00050.","journal-title":"Front Aging Neurosci"},{"key":"1645_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnagi.2018.00406","volume":"10","author":"A Kautzky","year":"2018","unstructured":"Kautzky A, Seiger R, Hahn A, Fischer P, Krampla W, Kasper S, et al. Prediction of autopsy verified neuropathological change of Alzheimer\u2019s disease using machine learning and MRI. Front Aging Neurosci. 2018;10:1\u201311. https:\/\/doi.org\/10.3389\/fnagi.2018.00406.","journal-title":"Front Aging Neurosci"},{"key":"1645_CR21","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.nicl.2018.08.019","volume":"20","author":"JM Mateos-P\u00e9rez","year":"2018","unstructured":"Mateos-P\u00e9rez JM, Dadar M, Lacalle-Aurioles M, Iturria-Medina Y, Zeighami Y, Evans AC. Structural neuroimaging as clinical predictor: A review of machine learning applications. NeuroImage Clin. 2018;20:506\u201322. https:\/\/doi.org\/10.1016\/j.nicl.2018.08.019.","journal-title":"NeuroImage Clin"},{"key":"1645_CR22","doi-asserted-by":"publisher","first-page":"734","DOI":"10.2174\/156720512801322627","volume":"9","author":"LL Barnes","year":"2012","unstructured":"Barnes LL, Shah RC, Aggarwal NT, Bennett DA, Schneider JA. The Minority Aging Research Study: Ongoing efforts to obtain brain donation in African Americans without dementia. Curr Alzheimer Res. 2012;9:734\u201345. https:\/\/doi.org\/10.2174\/156720512801322627.","journal-title":"Curr Alzheimer Res"},{"key":"1645_CR23","first-page":"270","volume":"18","author":"D Beekly","year":"2004","unstructured":"Beekly D, Ramos E, van Belle G, Deitrich W, Clark A, Jacka M, et al. The national Alzheimer\u2019s coordinating center (NACC) database: an Alzheimer disease database. Alzheimer Dis Assoc Disord. 2004;18:270\u20137.","journal-title":"Alzheimer Dis Assoc Disord"},{"key":"1645_CR24","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1093\/jnen\/nly049","volume":"77","author":"L Besser","year":"2018","unstructured":"Besser L, Kukull W, Teylan M, Bigio E, Cairns N, Kofler J, et al. The revised National Alzheimer\u2019s Coordinating Center\u2019s Neuropathology Form\u2014available data and new analyses. J Neuropathol Exp Neurol. 2018;77:717\u201326.","journal-title":"J Neuropathol Exp Neurol"},{"key":"1645_CR25","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1097\/WAD.0000000000000279","volume":"32","author":"L Besser","year":"2018","unstructured":"Besser L, Kukull W, Knopman D, Chui H, Galasko D, Weintraub S, et al. Version 3 of the national Alzheimer\u2019s coordinating center\u2019s uniform data set. Alzheimer Dis Assoc Disord. 2018;32:351\u20138.","journal-title":"Alzheimer Dis Assoc Disord"},{"key":"1645_CR26","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/TMI.2007.906087","volume":"27","author":"P Coupe","year":"2008","unstructured":"Coupe P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging. 2008;27:425\u201341. https:\/\/doi.org\/10.1109\/TMI.2007.906087.","journal-title":"IEEE Trans Med Imaging"},{"key":"1645_CR27","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/42.668698","volume":"17","author":"JG Sled","year":"1998","unstructured":"Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in mri data. IEEE Trans Med Imaging. 1998;17:87\u201397. https:\/\/doi.org\/10.1109\/42.668698.","journal-title":"IEEE Trans Med Imaging"},{"key":"1645_CR28","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neuroimage.2018.03.025","volume":"174","author":"M Dadar","year":"2018","unstructured":"Dadar M, Fonov VS, Collins DL. A comparison of publicly available linear MRI stereotaxic registration techniques. Neuroimage. 2018;174:191\u2013200. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.03.025.","journal-title":"Neuroimage"},{"key":"1645_CR29","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.neuroimage.2010.07.033","volume":"54","author":"V Fonov","year":"2011","unstructured":"Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage. 2011;54:313\u201327. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.07.033.","journal-title":"Neuroimage"},{"key":"1645_CR30","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.neuroimage.2017.06.009","volume":"157","author":"M Dadar","year":"2017","unstructured":"Dadar M, Maranzano J, Misquitta K, Anor CJ, Fonov VS, Tartaglia MC, et al. Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging. Neuroimage. 2017;157:233\u201349. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.06.009.","journal-title":"Neuroimage"},{"key":"1645_CR31","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.bpsc.2020.03.006","volume":"6","author":"CJ Anor","year":"2021","unstructured":"Anor CJ, Dadar M, Collins DL, Tartaglia MC. The Longitudinal Assessment of Neuropsychiatric Symptoms in Mild Cognitive Impairment and Alzheimer\u2019s Disease and Their Association With White Matter Hyperintensities in the National Alzheimer\u2019s Coordinating Center\u2019s Uniform Data Set. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021;6:70\u20138. https:\/\/doi.org\/10.1016\/j.bpsc.2020.03.006.","journal-title":"Biol Psychiatry Cogn Neurosci Neuroimaging"},{"key":"1645_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/dad2.12091","volume":"12","author":"M Dadar","year":"2020","unstructured":"Dadar M, Camicioli R, Duchesne S, Collins DL. The temporal relationships between white matter hyperintensities, neurodegeneration, amyloid beta, and cognition. Alzheimer\u2019s Dement Diagnosis, Assess Dis Monit. 2020;12:1\u201311. https:\/\/doi.org\/10.1002\/dad2.12091.","journal-title":"Alzheimer\u2019s Dement Diagnosis, Assess Dis Monit"},{"key":"1645_CR33","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.neurobiolaging.2018.12.004","volume":"76","author":"M Dadar","year":"2019","unstructured":"Dadar M, Maranzano J, Ducharme S, Collins DL. White matter in different regions evolves differently during progression to dementia. Neurobiol Aging. 2019;76:71\u20139. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2018.12.004.","journal-title":"Neurobiol Aging"},{"key":"1645_CR34","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1002\/hbm.23894","volume":"39","author":"M Dadar","year":"2018","unstructured":"Dadar M, Maranzano J, Ducharme S, Carmichael OT, Decarli C, Collins DL. Validation of T1w-based segmentations of white matter hyperintensity volumes in large-scale datasets of aging. Hum Brain Mapp. 2018;39:1093\u2013107. https:\/\/doi.org\/10.1002\/hbm.23894.","journal-title":"Hum Brain Mapp"},{"key":"1645_CR35","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl B. Freesurfer Neuroimage. 2012;62:774\u201381.","journal-title":"Freesurfer Neuroimage"},{"key":"1645_CR36","doi-asserted-by":"crossref","unstructured":"Beekly D, Schwabe\u2010Fry, K Bollenbeck, M Thomas G, DeCarli, CS Carmichael, OT Kukull W. [P1\u2013405]: The National Alzheimer\u2019s Coordinating Center: development of the MRI, PET and CSF biomarker databases. Alzheimer\u2019s Dement 2017;13:429.","DOI":"10.1016\/j.jalz.2017.06.2597"},{"key":"1645_CR37","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s40708-023-00213-8","volume":"10","author":"P Bhattarai","year":"2023","unstructured":"Bhattarai P, Taha A, Soni B, Thakuri DS, Ritter E, Chand GB. Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning. Brain Informatics. 2023;10:33.","journal-title":"Brain Informatics"},{"key":"1645_CR38","doi-asserted-by":"crossref","unstructured":"Feurer, M., Klein, A., Eggensperger K, Springenberg JT, Blum M, Hutter F. Auto-sklearn: efficient and robust automated machine learning. Autom Mach Learn, 2019:113\u201334.","DOI":"10.1007\/978-3-030-05318-5_6"},{"key":"1645_CR39","unstructured":"Kumari R, Singh J, Gosain A. International Conference on Information and Communication Technology for Intelligent Systems. Singapore Springer Nat Singapore 2023:145\u201353."},{"key":"1645_CR40","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neuroimage.2016.10.038","volume":"145","author":"G Varoquaux","year":"2017","unstructured":"Varoquaux G, Raamana PR, Engemann DA, Hoyos-Idrobo A, Schwartz Y, Thirion B. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. Neuroimage. 2017;145:166\u201379.","journal-title":"Neuroimage"},{"key":"1645_CR41","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1002\/ana.72","volume":"49","author":"S Tekin","year":"2001","unstructured":"Tekin S, Mega MS, Masterman DM, Chow T, Garakian J, Vinters HV, et al. Orbitofrontal and anterior cingulate cortex neurofibrillary tangle burden is associated with agitation in Alzheimer disease. Ann Neurol. 2001;49:355\u201361. https:\/\/doi.org\/10.1002\/ana.72.","journal-title":"Ann Neurol"},{"key":"1645_CR42","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1016\/j.neuroimage.2009.04.023","volume":"47","author":"S Duchesne","year":"2009","unstructured":"Duchesne S, Caroli A, Geroldi C, Collins DL, Frisoni GB. Relating one-year cognitive change in mild cognitive impairment to baseline MRI features. Neuroimage. 2009;47:1363\u201370. https:\/\/doi.org\/10.1016\/j.neuroimage.2009.04.023.","journal-title":"Neuroimage"},{"key":"1645_CR43","unstructured":"Fox J. Applied Regression Analysis and Generalized Linear Models Third Edition. Appl Regres Anal Gen Linear Model 2nd Ed 2015;25."},{"key":"1645_CR44","doi-asserted-by":"publisher","unstructured":"Mahajan P, Uddin S, Hajati F, Moni MA. Ensemble Learning for Disease Prediction: A Review. Healthc 2023;11. https:\/\/doi.org\/10.3390\/healthcare11121808.","DOI":"10.3390\/healthcare11121808"},{"key":"1645_CR45","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1002\/sam.10141.A","volume":"4","author":"S Chen","year":"2011","unstructured":"Chen S, Bowman FD. A Novel Support Vector Classifier for Longitudinal High- dimensional Data and Its Application to Neuroimaging Data. Stat Anal Data Min ASA Data Sci J. 2011;4:604\u201311. https:\/\/doi.org\/10.1002\/sam.10141.A.","journal-title":"Stat Anal Data Min ASA Data Sci J"},{"key":"1645_CR46","doi-asserted-by":"crossref","unstructured":"Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289\u2013300.","DOI":"10.1111\/j.2517-6161.1995.tb02031.x"},{"key":"1645_CR47","doi-asserted-by":"publisher","first-page":"102121","DOI":"10.1016\/j.nicl.2019.102121","volume":"25","author":"A Zandifar","year":"2020","unstructured":"Zandifar A, Fonov VS, Ducharme S, Belleville S, Collins DL. MRI and cognitive scores complement each other to accurately predict Alzheimer\u2019s dementia 2 to 7 years before clinical onset. NeuroImage Clin. 2020;25:102121. https:\/\/doi.org\/10.1016\/j.nicl.2019.102121.","journal-title":"NeuroImage Clin"},{"key":"1645_CR48","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1159\/000506124","volume":"48","author":"A Kaskikallio","year":"2020","unstructured":"Kaskikallio A, Karrasch M, Koikkalainen J, L\u00f6tj\u00f6nen J, Rinne JO, Tuokkola T, et al. White Matter Hyperintensities and Cognitive Impairment in Healthy and Pathological Aging: A Quantified Brain MRI Study. Dement Geriatr Cogn Disord. 2020;48:297\u2013307. https:\/\/doi.org\/10.1159\/000506124.","journal-title":"Dement Geriatr Cogn Disord"},{"key":"1645_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/20515960165","volume":"2","author":"JB Toledo","year":"2014","unstructured":"Toledo JB, Cairns NJ, Da X, Chen K, Carter D, Fleisher A, et al. Clinical and multimodal biomarker correlates of ADNI neuropathological findings. Acta Neuropathol Commun. 2014;2:1\u201313. https:\/\/doi.org\/10.1186\/20515960165.","journal-title":"Acta Neuropathol Commun"}],"container-title":["GeroScience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11357-025-01645-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11357-025-01645-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11357-025-01645-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T14:02:06Z","timestamp":1750514526000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11357-025-01645-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,8]]},"references-count":49,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["1645"],"URL":"https:\/\/doi.org\/10.1007\/s11357-025-01645-2","relation":{},"ISSN":["2509-2723"],"issn-type":[{"value":"2509-2723","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,8]]},"assertion":[{"value":"10 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Written informed consent was obtained from participants or their study partner.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent Statement"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors report no disclosures relevant to the manuscript.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosures"}}]}}