{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T02:18:11Z","timestamp":1789179491057,"version":"build-2803163510"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Transl Psychiatry"],"DOI":"10.1038\/s41398-024-03097-2","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T10:04:17Z","timestamp":1727431457000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Magnitude and kinetics of a set of neuroanatomic volume and thickness together with white matter hyperintensity is definitive of cognitive status and brain age"],"prefix":"10.1038","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0459-6336","authenticated-orcid":false,"given":"Neha","family":"Yadav","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3613-0827","authenticated-orcid":false,"given":"Niraj Kumar","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darshit","family":"Thakar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8009-4622","authenticated-orcid":false,"given":"Vivek","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"3097_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jpsychires.2018.02.008","volume":"100","author":"Y Stephan","year":"2018","unstructured":"Stephan Y, Sutin AR, Luchetti M, Terracciano A. Subjective age and risk of incident dementia: evidence from the National Health and Aging Trends survey. J Psychiatr Res. 2018;100:1\u20134.","journal-title":"J Psychiatr Res"},{"key":"3097_CR2","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1212\/WNL.0b013e3182166e96","volume":"76","author":"BC Dickerson","year":"2011","unstructured":"Dickerson BC, Stoub TR, Shah RC, Sperling RA, Killiany RJ, Albert MS, et al. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology. 2011;76:1395\u2013402.","journal-title":"Neurology"},{"key":"3097_CR3","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1212\/WNL.48.5.1297","volume":"48","author":"JA Kaye","year":"1997","unstructured":"Kaye JA, Swihart T, Howieson D, Dame A, Moore MM, Karnos T, et al. Volume loss of the hippocampus and temporal lobe in healthy elderly persons destined to develop dementia. Neurology. 1997;48:1297\u2013304.","journal-title":"Neurology"},{"key":"3097_CR4","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1136\/pgmj.2005.036665","volume":"82","author":"R Peters","year":"2006","unstructured":"Peters R. Ageing and the brain. Postgrad Med J. 2006;82:84\u201388.","journal-title":"Postgrad Med J"},{"key":"3097_CR5","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1093\/cercor\/7.3.268","volume":"7","author":"N Raz","year":"1997","unstructured":"Raz N. Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cereb Cortex. 1997;7:268\u201382.","journal-title":"Cereb Cortex"},{"key":"3097_CR6","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1001\/archneur.60.7.989","volume":"60","author":"RI Scahill","year":"2003","unstructured":"Scahill RI, Frost C, Jenkins R, Whitwell JL, Rossor MN, Fox NC. A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch Neurol. 2003;60:989.","journal-title":"Arch Neurol"},{"key":"3097_CR7","doi-asserted-by":"publisher","first-page":"e1917257","DOI":"10.1001\/jamanetworkopen.2019.17257","volume":"2","author":"H Suzuki","year":"2019","unstructured":"Suzuki H, Venkataraman AV, Bai W, Guitton F, Guo Y, Dehghan A, et al. Associations of regional brain structural differences with aging, modifiable risk factors for dementia, and cognitive performance. JAMA Netw Open. 2019;2:e1917257.","journal-title":"JAMA Netw Open"},{"key":"3097_CR8","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/j.neurobiolaging.2009.05.003","volume":"32","author":"Y Taki","year":"2011","unstructured":"Taki Y, Kinomura S, Sato K, Goto R, Kawashima R, Fukuda H. A longitudinal study of gray matter volume decline with age and modifying factors. Neurobiol Aging. 2011;32:907\u201315.","journal-title":"Neurobiol Aging"},{"key":"3097_CR9","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1046\/j.1440-1614.2001.00969.x","volume":"35","author":"JN Trollor","year":"2001","unstructured":"Trollor JN, Valenzuela MJ. Brain ageing in the new millennium. Aust NZ J Psychiatry. 2001;35:788\u2013805.","journal-title":"Aust NZ J Psychiatry"},{"key":"3097_CR10","doi-asserted-by":"crossref","unstructured":"Wardlaw JM, Vald\u00e9s Hern\u00e1ndez MC, Mu\u00f1oz\u2010Maniega S. What are white matter hyperintensities made of? J Am Heart Assoc 2015;4:001140.","DOI":"10.1161\/JAHA.114.001140"},{"key":"3097_CR11","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","volume":"7","author":"MS Albert","year":"2011","unstructured":"Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging\u2010Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease. Alzheimer\u2019s Dement. 2011;7:270\u20139.","journal-title":"Alzheimer\u2019s Dement"},{"key":"3097_CR12","doi-asserted-by":"crossref","unstructured":"Chen Y-X, Liang N, Li X-L, Yang S-H, Wang Y-P, Shi N-N. Diagnosis and treatment for mild cognitive impairment: a systematic review of clinical practice guidelines and consensus statements. Front Neurol 2021;12:719849.","DOI":"10.3389\/fneur.2021.719849"},{"key":"3097_CR13","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.cger.2014.04.001","volume":"30","author":"J Hugo","year":"2014","unstructured":"Hugo J, Ganguli M. Dementia and cognitive impairment. Clin Geriatr Med. 2014;30:421\u201342.","journal-title":"Clin Geriatr Med"},{"key":"3097_CR14","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1001\/jama.2014.13806","volume":"312","author":"KM Langa","year":"2014","unstructured":"Langa KM, Levine DA. The diagnosis and management of mild cognitive impairment. JAMA. 2014;312:2551.","journal-title":"JAMA"},{"key":"3097_CR15","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.jalz.2011.03.005","volume":"7","author":"GM McKhann","year":"2011","unstructured":"McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, et al. The diagnosis of dementia due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging\u2010Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease. Alzheimer\u2019s Dement. 2011;7:263\u20139.","journal-title":"Alzheimer\u2019s Dement"},{"key":"3097_CR16","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.trci.2018.09.008","volume":"4","author":"RL Nosheny","year":"2018","unstructured":"Nosheny RL, Camacho MR, Insel PS, Flenniken D, Fockler J, Truran D, et al. Online study partner\u2010reported cognitive decline in the Brain Health Registry. Alzheimer\u2019s Dement: Transl Res Clin Interv. 2018;4:565\u201374.","journal-title":"Alzheimer\u2019s Dement: Transl Res Clin Interv"},{"key":"3097_CR17","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1016\/j.jalz.2014.09.002","volume":"11","author":"AD Rueda","year":"2015","unstructured":"Rueda AD, Lau KM, Saito N, Harvey D, Risacher SL, Aisen PS, et al. Self\u2010rated and informant\u2010rated everyday function in comparison to objective markers of Alzheimer\u2019s disease. Alzheimer\u2019s Dement. 2015;11:1080\u20139.","journal-title":"Alzheimer\u2019s Dement"},{"key":"3097_CR18","unstructured":"Kumar Gupta N, Yadav N, Aman A, Tiwari V. Brain WMH Load, kinetics and regional distribution with aging: a signature of structural and cognitive health. International Society for Magnetic Resonance in Medicine, 2003."},{"key":"3097_CR19","unstructured":"Yadav N, Majumdar A, Tiwari V. MRI measured neuroanatomic volume, cortical thinning and Wmh load with aging: the early, intermediate and late events of cognitive status. In: Imaging Aging, Dementia & Alzheimer\u2019s Disease I. International Society for Magnetic Resonance in Medicine, 2003."},{"key":"3097_CR20","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1097\/WAD.0b013e318142774e","volume":"21","author":"DL Beekly","year":"2007","unstructured":"Beekly DL, Ramos EM, Lee WW, Deitrich WD, Jacka ME, Wu J, et al. The National Alzheimer\u2019s Coordinating Center (NACC) Database: The Uniform Data Set. Alzheimer Dis Assoc Disord. 2007;21:249\u201358.","journal-title":"Alzheimer Dis Assoc Disord"},{"key":"3097_CR21","first-page":"270","volume":"18","author":"DL Beekly","year":"2004","unstructured":"Beekly DL, Ramos EM, van Belle G, Deitrich W, Clark AD, Jacka ME, 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":"3097_CR22","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 DS, 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":"3097_CR23","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.parkreldis.2019.02.004","volume":"63","author":"H-P M\u00fcller","year":"2019","unstructured":"M\u00fcller H-P, Huppertz H-J, Dreyhaupt J, Ludolph AC, Tabrizi SJ, Roos RAC, et al. Combined cerebral atrophy score in Huntington\u2019s disease based on atlas-based MRI volumetry: sample size calculations for clinical trials. Parkinsonism Relat Disord. 2019;63:179\u201384.","journal-title":"Parkinsonism Relat Disord"},{"key":"3097_CR24","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.4103\/0028-3886.329528","volume":"69","author":"SM Thota","year":"2021","unstructured":"Thota SM, Chan KL, Pradhan SS, Nagabushana B, Priyanka GB, Sunil HV, et al. Multimodal imaging and visual evoked potentials reveal key structural and functional features that distinguish symptomatic from presymptomatic Huntington\u2019s disease Brain. Neurol India. 2021;69:1247\u201358.","journal-title":"Neurol India"},{"key":"3097_CR25","doi-asserted-by":"crossref","unstructured":"Bates D, M\u00e4chler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw 2015;67:1\u201348.","DOI":"10.18637\/jss.v067.i01"},{"key":"3097_CR26","unstructured":"R Core Team. R: a language and environment for statistical computing. 2021."},{"key":"3097_CR27","first-page":"23","volume":"14","author":"EC Alexopoulos","year":"2010","unstructured":"Alexopoulos EC. Introduction to multivariate regression analysis. Hippokratia. 2010;14:23\u20138.","journal-title":"Hippokratia"},{"key":"3097_CR28","doi-asserted-by":"crossref","unstructured":"Shafer AT, Williams OA, Perez E, An Y, Landman BA, Ferrucci L et al. Accelerated decline in white matter microstructure in subsequently impaired older adults and its relationship with cognitive decline. Brain Commun 2022;4:fcac051.","DOI":"10.1093\/braincomms\/fcac051"},{"key":"3097_CR29","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1001\/jama.2017.0627","volume":"317","author":"AH Cook","year":"2017","unstructured":"Cook AH, Sridhar J, Ohm D, Rademaker A, Mesulam M-M, Weintraub S, et al. Rates of cortical atrophy in adults 80 years and older with superior vs average episodic memory. JAMA. 2017;317:1373.","journal-title":"JAMA"},{"key":"3097_CR30","doi-asserted-by":"publisher","first-page":"2357","DOI":"10.1007\/s11357-013-9510-6","volume":"35","author":"N Geifman","year":"2013","unstructured":"Geifman N, Cohen R, Rubin E. Redefining meaningful age groups in the context of disease. Age (Omaha). 2013;35:2357\u201366.","journal-title":"Age (Omaha)"},{"key":"3097_CR31","doi-asserted-by":"crossref","unstructured":"Burchett WW, Ellis AR, Harrar SW, Bathke AC. Nonparametric inference for multivariate data: the R Package npmv. J Stat Softw 2017;76:1\u201318.","DOI":"10.18637\/jss.v076.i04"},{"key":"3097_CR32","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.3233\/JAD-180017","volume":"63","author":"ML Alosco","year":"2018","unstructured":"Alosco ML, Sugarman MA, Besser LM, Tripodis Y, Martin B, Palmisano JN, et al. A clinicopathological investigation of white matter hyperintensities and Alzheimer\u2019s disease neuropathology. J Alzheimer\u2019s Dis. 2018;63:1347\u201360.","journal-title":"J Alzheimer\u2019s Dis"},{"key":"3097_CR33","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.neuroimage.2018.03.050","volume":"174","author":"J Jiang","year":"2018","unstructured":"Jiang J, Liu T, Zhu W, Koncz R, Liu H, Lee T, et al. UBO detector \u2013 a cluster-based, fully automated pipeline for extracting white matter hyperintensities. Neuroimage. 2018;174:539\u201349.","journal-title":"Neuroimage"},{"key":"3097_CR34","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"3097_CR35","unstructured":"Amr T. Hands-on machine learning with scikit-learn and scientific Python toolkits: a practical guide to implementing supervised and unsupervised machine learning algorithms in Python. Packt Publishing, Limited: Birmingham,UK, 2020."},{"key":"3097_CR36","doi-asserted-by":"publisher","first-page":"20","DOI":"10.38094\/jastt20165","volume":"2","author":"B Charbuty","year":"2021","unstructured":"Charbuty B, Abdulazeez A. Classification based on decision tree algorithm for machine learning. J Appl Sci Technol Trends. 2021;2:20\u201328.","journal-title":"J Appl Sci Technol Trends"},{"key":"3097_CR37","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random Forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"3097_CR38","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L. Bagging predictors. Mach Learn. 1996;24:123\u201340.","journal-title":"Mach Learn"},{"key":"3097_CR39","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.inffus.2020.07.007","volume":"64","author":"S Gonz\u00e1lez","year":"2020","unstructured":"Gonz\u00e1lez S, Garc\u00eda S, Del Ser J, Rokach L, Herrera F. A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities. Inf Fusion. 2020;64:205\u201337.","journal-title":"Inf Fusion"},{"key":"3097_CR40","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM: New York, NY, USA, 2016, pp 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"key":"3097_CR41","doi-asserted-by":"publisher","first-page":"189","DOI":"10.4258\/hir.2021.27.3.189","volume":"27","author":"I Tougui","year":"2021","unstructured":"Tougui I, Jilbab A, Mhamdi JE. Impact of the choice of cross-validation techniques on the results of machine learning-based diagnostic applications. Health Inf Res. 2021;27:189\u201399.","journal-title":"Health Inf Res"},{"key":"3097_CR42","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":"Neuroimage"},{"key":"3097_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2021.103600","volume":"72","author":"L Baecker","year":"2021","unstructured":"Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A. Machine learning for brain age prediction: introduction to methods and clinical applications. EBioMedicine. 2021;72:103600.","journal-title":"EBioMedicine"},{"key":"3097_CR44","first-page":"34","volume":"25","author":"MF Mendez","year":"2019","unstructured":"Mendez MF. Early-onset Alzheimer disease and its variants. Continum. 2019;25:34\u201351.","journal-title":"Continum"},{"key":"3097_CR45","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1159\/000504341","volume":"48","author":"ML Phillips","year":"2019","unstructured":"Phillips ML, Stage EC Jr, Lane KA, Gao S, Risacher SL, Goukasian N, et al. Neurodegenerative patterns of cognitive clusters of early-onset Alzheimer\u2019s disease subjects: evidence for disease heterogeneity. Dement Geriatr Cogn Disord. 2019;48:131\u201342.","journal-title":"Dement Geriatr Cogn Disord"},{"key":"3097_CR46","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neurobiolaging.2015.11.001","volume":"38","author":"S Krumm","year":"2016","unstructured":"Krumm S, Kivisaari SL, Probst A, Monsch AU, Reinhardt J, Ulmer S, et al. Cortical thinning of parahippocampal subregions in very early Alzheimer\u2019s disease. Neurobiol Aging. 2016;38:188\u201396.","journal-title":"Neurobiol Aging"},{"key":"3097_CR47","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1002\/alz.12108","volume":"16","author":"MM Machulda","year":"2020","unstructured":"Machulda MM, Lundt ES, Albertson SM, Spychalla AJ, Schwarz CG, Mielke MM, et al. Cortical atrophy patterns of incident MCI subtypes in the mayo clinic study of aging. Alzheimer\u2019s Dement. 2020;16:1013\u201322.","journal-title":"Alzheimer\u2019s Dement"},{"key":"3097_CR48","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.nicl.2015.01.017","volume":"7","author":"E Mak","year":"2015","unstructured":"Mak E, Su L, Williams GB, Watson R, Firbank M, Blamire AM, et al. Longitudinal assessment of global and regional atrophy rates in Alzheimer\u2019s disease and dementia with Lewy bodies. Neuroimage Clin. 2015;7:456\u201362.","journal-title":"Neuroimage Clin"},{"key":"3097_CR49","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1148\/radiol.2482070938","volume":"248","author":"JD Sluimer","year":"2008","unstructured":"Sluimer JD, van der Flier WM, Karas GB, Fox NC, Scheltens P, Barkhof F, et al. Whole-brain atrophy rate and cognitive decline: longitudinal MR study of memory clinic patients. Radiology. 2008;248:590\u20138.","journal-title":"Radiology"},{"key":"3097_CR50","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.1093\/brain\/awm021","volume":"130","author":"JL Whitwell","year":"2006","unstructured":"Whitwell JL, Jack CR, Parisi JE, Knopman DS, Boeve BF, Petersen RC, et al. Rates of cerebral atrophy differ in different degenerative pathologies. Brain. 2006;130:1148\u201358.","journal-title":"Brain"},{"key":"3097_CR51","doi-asserted-by":"crossref","unstructured":"Franke K, Gaser C. Ten years of BrainAGE as a neuroimaging biomarker of brain aging: what insights have we gained? Front Neurol 2019;10:789.","DOI":"10.3389\/fneur.2019.00789"}],"container-title":["Translational Psychiatry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41398-024-03097-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41398-024-03097-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41398-024-03097-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T10:04:24Z","timestamp":1727431464000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41398-024-03097-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["3097"],"URL":"https:\/\/doi.org\/10.1038\/s41398-024-03097-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.09.05.556337","asserted-by":"object"}]},"ISSN":["2158-3188"],"issn-type":[{"value":"2158-3188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,27]]},"assertion":[{"value":"28 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"ADRCs have been contributing data to the UDS at NACC since 2005 [\n                      \n                      \u2013\n                      \n                      ], including participants with cognitive status ranging from no cognitive impairment to dementia. The ADRCs obtain the written informed consent from the participants based on the IRB approval of each ADRCs (\n                      \n                      ). All NACC data is freely available to researchers. To receive the NACC data a request is submitted by the user (\n                      \n                      ). For the present study, we at IISER Berhampur received anonymized UDS and MRI from the NACC ADRCs (Data Request ID #5530) on 22 October 2021, for our analysis.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"389"}}