{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T10:49:54Z","timestamp":1789987794064,"version":"4.0.1"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2019,4,6]],"date-time":"2019-04-06T00:00:00Z","timestamp":1554508800000},"content-version":"am","delay-in-days":279,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["TEC2014-52289R"],"award-info":[{"award-number":["TEC2014-52289R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["TEC2016-81900-REDT\/AEI"],"award-info":[{"award-number":["TEC2016-81900-REDT\/AEI"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["TEC2017-83838-R"],"award-info":[{"award-number":["TEC2017-83838-R"]}],"id":[{"id":"10.13039\/501100003329","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":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1016\/j.mri.2018.03.003","type":"journal-article","created":{"date-parts":[[2018,3,12]],"date-time":"2018-03-12T07:23:41Z","timestamp":1520839421000},"page":"84-95","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":37,"special_numbering":"C","title":["Comparison of feature representations in MRI-based MCI-to-AD conversion prediction"],"prefix":"10.1016","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7702-8747","authenticated-orcid":false,"given":"Marta","family":"G\u00f3mez-Sancho","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1048-5860","authenticated-orcid":false,"given":"Jussi","family":"Tohka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"G\u00f3mez-Verdejo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.mri.2018.03.003_bb0005","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s004010050508","article-title":"Development of Alzheimer-related neurofibrillary changes in the neocortex inversely recapitulates cortical myelogenesis","volume":"92","author":"Braak","year":"1996","journal-title":"Acta Neuropathol"},{"issue":"6","key":"10.1016\/j.mri.2018.03.003_bb0010","doi-asserted-by":"crossref","DOI":"10.1212\/WNL.52.6.1158","article-title":"The biochemical pathway of neurofibrillary degeneration in aging and Alzheimers Disease","volume":"52","author":"Delacourte","year":"1999","journal-title":"Neurology"},{"issue":"3","key":"10.1016\/j.mri.2018.03.003_bb0015","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1212\/WNL.46.3.707","article-title":"Cerebral amyloid deposition and diffuse plaques in \u201cnormal\u201d aging evidence for presymptomatic and very mild Alzheimer's Disease","volume":"46","author":"Morris","year":"1996","journal-title":"Neurology"},{"issue":"1","key":"10.1016\/j.mri.2018.03.003_bb0020","doi-asserted-by":"crossref","first-page":"a006189","DOI":"10.1101\/cshperspect.a006189","article-title":"Neuropathological alterations in Alzheimer Disease","volume":"1","author":"Serrano-Pozo","year":"2011","journal-title":"Cold Spring Harb Perspect Med"},{"issue":"1","key":"10.1016\/j.mri.2018.03.003_bb0025","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.exger.2006.05.016","article-title":"Early detection of Alzheimers Disease using neuroimaging","volume":"42","author":"Mosconi","year":"2007","journal-title":"Exp Gerontol"},{"issue":"3","key":"10.1016\/j.mri.2018.03.003_bb0030","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1111\/joim.12190","article-title":"Mild cognitive impairment: a concept in evolution","volume":"275","author":"Petersen","year":"2014","journal-title":"J Intern Med"},{"issue":"5","key":"10.1016\/j.mri.2018.03.003_bb0035","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1093\/brain\/awv029","article-title":"Prevalence and prognosis of Alzheimers Disease at the mild cognitive impairment stage","volume":"138","author":"Vos","year":"2015","journal-title":"Brain"},{"issue":"4","key":"10.1016\/j.mri.2018.03.003_bb0040","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1111\/j.1600-0447.2008.01326.x","article-title":"Rate of progression of mild cognitive impairment to dementia \u2014 meta-analysis of 41 robust inception cohort studies","volume":"119","author":"Mitchell","year":"2009","journal-title":"Acta Psychiatr Scand"},{"issue":"10","key":"10.1016\/j.mri.2018.03.003_bb0045","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1016\/S1474-4422(15)00093-9","article-title":"Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection","volume":"14","author":"Teipel","year":"2015","journal-title":"Lancet Neurol"},{"key":"10.1016\/j.mri.2018.03.003_bb0050","article-title":"Recent publications from the Alzheimer's disease neuroimaging initiative: reviewing progress toward improved ad clinical trials","author":"Weiner","year":"2017","journal-title":"Alzheimers Dement"},{"issue":"4","key":"10.1016\/j.mri.2018.03.003_bb0055","doi-asserted-by":"crossref","first-page":"a006213","DOI":"10.1101\/cshperspect.a006213","article-title":"Brain imaging in Alzheimer Disease","volume":"2","author":"Johnson","year":"2012","journal-title":"Cold Spring Harb Perspect Med"},{"issue":"2","key":"10.1016\/j.mri.2018.03.003_bb0060","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/nrneurol.2009.215","article-title":"The clinical use of structural MRI in Alzheimer Disease","volume":"6","author":"Frisoni","year":"2010","journal-title":"Nat Rev Neurol"},{"issue":"3","key":"10.1016\/j.mri.2018.03.003_bb0065","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1093\/brain\/awm319","article-title":"Automatic classification of MR scans in Alzheimer's Disease","volume":"131","author":"Kl\u00f6ppel","year":"2008","journal-title":"Brain"},{"issue":"12","key":"10.1016\/j.mri.2018.03.003_bb0070","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.1016\/j.neurobiolaging.2013.06.015","article-title":"How early can we predict Alzheimer's Disease using computational anatomy?","volume":"34","author":"Adaszewski","year":"2013","journal-title":"Neurobiol Aging"},{"key":"10.1016\/j.mri.2018.03.003_bb0075","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.neuroimage.2012.09.058","article-title":"Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning","volume":"65","author":"Eskildsen","year":"2013","journal-title":"NeuroImage"},{"key":"10.1016\/j.mri.2018.03.003_bb0080","doi-asserted-by":"crossref","DOI":"10.3389\/fninf.2011.00022","article-title":"High dimensional classification of structural MRI Alzheimers Disease data based on large scale regularization","volume":"5","author":"Casanova","year":"2011","journal-title":"Front Neuroinform"},{"issue":"6","key":"10.1016\/j.mri.2018.03.003_bb0085","doi-asserted-by":"crossref","first-page":"e67346","DOI":"10.1371\/journal.pone.0067346","article-title":"BrainAGE in mild cognitive impaired patients: predicting the conversion to Alzheimers Disease","volume":"8","author":"Gaser","year":"2013","journal-title":"PloS ONE"},{"issue":"10","key":"10.1016\/j.mri.2018.03.003_bb0090","doi-asserted-by":"crossref","first-page":"e25446","DOI":"10.1371\/journal.pone.0025446","article-title":"Multi-method analysis of MRI images in early diagnostics of Alzheimer's Disease","volume":"6","author":"Wolz","year":"2011","journal-title":"PloS one"},{"issue":"1","key":"10.1016\/j.mri.2018.03.003_bb0095","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.neuroimage.2012.04.056","article-title":"Combining MRI and CSF measures for classification of Alzheimer's Disease and prediction of Mild Cognitive Impairment conversion","volume":"62","author":"Westman","year":"2012","journal-title":"Neuroimage"},{"issue":"4","key":"10.1016\/j.mri.2018.03.003_bb0100","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1111\/jon.12163","article-title":"Predictive models based on Support Vector Machines: whole-brain versus regional analysis of structural MRI in the Alzheimer's Disease","volume":"25","author":"Retico","year":"2015","journal-title":"J Neuroimaging"},{"issue":"3","key":"10.1016\/j.mri.2018.03.003_bb0105","doi-asserted-by":"crossref","first-page":"546","DOI":"10.3174\/ajnr.A5061","article-title":"Predictive utility of marketed volumetric software tools in subjects at risk for Alzheimer Disease: do regions outside the hippocampus matter?","volume":"38","author":"Tanpitukpongse","year":"2017","journal-title":"Am J Neuroradiol"},{"key":"10.1016\/j.mri.2018.03.003_bb0110","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.neuroimage.2017.03.057","article-title":"A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages","volume":"155","author":"Rathore","year":"2017","journal-title":"NeuroImage"},{"issue":"2","key":"10.1016\/j.mri.2018.03.003_bb0115","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s12021-013-9204-3","article-title":"A review of feature reduction techniques in neuroimaging","volume":"12","author":"Mwangi","year":"2014","journal-title":"Neuroinformatics"},{"issue":"2","key":"10.1016\/j.mri.2018.03.003_bb0120","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1016\/j.neuroimage.2010.08.044","article-title":"Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls","volume":"54","author":"Westman","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.mri.2018.03.003_bb0125","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1016\/j.nicl.2016.05.017","article-title":"A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity","volume":"11","author":"Schwarz","year":"2016","journal-title":"NeuroImage Clin"},{"key":"10.1016\/j.mri.2018.03.003_bb0130","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.neuroimage.2015.01.048","article-title":"Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the caddementia challenge","volume":"111","author":"Bron","year":"2015","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.mri.2018.03.003_bb0135","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s10548-012-0246-x","article-title":"Regional magnetic resonance imaging measures for multivariate analysis in Alzheimers disease and mild cognitive impairment","volume":"26","author":"Westman","year":"2013","journal-title":"Brain Topogr"},{"issue":"2","key":"10.1016\/j.mri.2018.03.003_bb0140","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1016\/j.neuroimage.2010.06.013","article-title":"Automatic classification of patients with Alzheimer's Disease from structural MRI: a comparison of ten methods using the ADNI database","volume":"56","author":"Cuingnet","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.mri.2018.03.003_bb0145","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's disease with a good between-cohort robustness","volume":"6","author":"Lebedev","year":"2014","journal-title":"NeuroImage Clin"},{"key":"10.1016\/j.mri.2018.03.003_bb0150","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's conversion prediction in MCI subjects","volume":"104","author":"Moradi","year":"2015","journal-title":"Neuroimage"},{"key":"10.1016\/j.mri.2018.03.003_bb0155","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s12021-015-9292-3","article-title":"Comparison of feature selection techniques in machine learning for anatomical brain MRI in dementia","volume":"14","author":"Tohka","year":"2016","journal-title":"Neuroinformatics"},{"issue":"1","key":"10.1016\/j.mri.2018.03.003_bb0160","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neuroimage.2011.11.066","article-title":"Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images","volume":"60","author":"Chu","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.mri.2018.03.003_bb0165","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.neuroimage.2017.04.018","article-title":"A comparison of accurate automatic hippocampal segmentation methods","volume":"155","author":"Zandifar","year":"2017","journal-title":"NeuroImage"},{"issue":"5","key":"10.1016\/j.mri.2018.03.003_bb0170","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1002\/hbm.23139","article-title":"Effects of change in freesurfer version on classification accuracy of patients with Alzheimer's disease and mild cognitive impairment","volume":"37","author":"Chepkoech","year":"2016","journal-title":"Hum Brain Mapp"},{"key":"10.1016\/j.mri.2018.03.003_bb0175","doi-asserted-by":"crossref","DOI":"10.3389\/fnagi.2014.00264","article-title":"The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease","volume":"6","author":"Voevodskaya","year":"2014","journal-title":"Front Aging Neurosci"},{"issue":"7","key":"10.1016\/j.mri.2018.03.003_bb0180","doi-asserted-by":"crossref","first-page":"e21896","DOI":"10.1371\/journal.pone.0021896","article-title":"Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors","volume":"6","author":"Cui","year":"2011","journal-title":"PloS one"},{"issue":"7","key":"10.1016\/j.mri.2018.03.003_bb0185","doi-asserted-by":"crossref","first-page":"e22193","DOI":"10.1371\/journal.pone.0022193","article-title":"Age correction in dementia-matching to a healthy brain","volume":"6","author":"Dukart","year":"2011","journal-title":"PloS one"},{"issue":"3","key":"10.1016\/j.mri.2018.03.003_bb0190","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.neuroimage.2010.01.005","article-title":"Estimating the age of healthy subjects from t1-weighted MRI scans using kernel methods: exploring the influence of various parameters","volume":"50","author":"Franke","year":"2010","journal-title":"Neuroimage"},{"issue":"1","key":"10.1016\/j.mri.2018.03.003_bb0195","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (roc) curve.","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"10.1016\/j.mri.2018.03.003_bb0200","series-title":"Proceedings of the Twenty-first International Conference on Machine Learning","first-page":"89","article-title":"Model selection via the AUC","author":"Rosset","year":"2004"},{"key":"10.1016\/j.mri.2018.03.003_bb0205","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for Support Vector Machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans Intell Systems Tech"},{"issue":"1","key":"10.1016\/j.mri.2018.03.003_bb0210","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J Stat Softw"},{"key":"10.1016\/j.mri.2018.03.003_bb0215","series-title":"The elements of statistical learning \u2014 data mining, inference, and prediction, Second Edition","author":"Hastie","year":"2009"},{"issue":"2","key":"10.1016\/j.mri.2018.03.003_bb0220","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J R Stat Soc Ser B"},{"issue":"2","key":"10.1016\/j.mri.2018.03.003_bb0225","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.pscychresns.2012.11.005","article-title":"Different multivariate techniques for automated classification of MRI data in Alzheimers disease and mild cognitive impairment","volume":"212","author":"Aguilar","year":"2013","journal-title":"Psychiatry Res Neuroimaging"},{"issue":"1","key":"10.1016\/j.mri.2018.03.003_bb0230","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"10.1016\/j.mri.2018.03.003_bb0235","article-title":"Ranger: A fast implementation of random forests for high dimensional data in C++ and R","volume":"77","author":"Wright","year":"2015","journal-title":"J Stat Softw"},{"key":"10.1016\/j.mri.2018.03.003_bb0240","series-title":"Advances in neural information processing systems","first-page":"307","article-title":"Inference for the generalization error","author":"Nadeau","year":"2000"},{"issue":"7","key":"10.1016\/j.mri.2018.03.003_bb0245","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate statistical tests for comparing supervised classification learning algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput"}],"container-title":["Magnetic Resonance Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0730725X18300286?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0730725X18300286?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T08:30:00Z","timestamp":1759998600000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0730725X18300286"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":49,"alternative-id":["S0730725X18300286"],"URL":"https:\/\/doi.org\/10.1016\/j.mri.2018.03.003","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/213132","asserted-by":"object"}]},"ISSN":["0730-725X"],"issn-type":[{"value":"0730-725X","type":"print"}],"subject":[],"published":{"date-parts":[[2018,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Comparison of feature representations in MRI-based MCI-to-AD conversion prediction","name":"articletitle","label":"Article Title"},{"value":"Magnetic Resonance Imaging","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.mri.2018.03.003","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2018 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}