{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:48:39Z","timestamp":1790596119110,"version":"4.1.0"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2018,5,1]],"date-time":"2018-05-01T00:00:00Z","timestamp":1525132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2018,5,1]],"date-time":"2018-05-01T00:00:00Z","timestamp":1525132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neuroscience Methods"],"published-print":{"date-parts":[[2018,5]]},"DOI":"10.1016\/j.jneumeth.2017.12.011","type":"journal-article","created":{"date-parts":[[2017,12,26]],"date-time":"2017-12-26T12:45:59Z","timestamp":1514292359000},"page":"3-9","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":123,"special_numbering":"C","title":["Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge"],"prefix":"10.1016","volume":"302","author":[{"given":"Nicola","family":"Amoroso","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Domenico","family":"Diacono","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annarita","family":"Fanizzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marianna","family":"La Rocca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfonso","family":"Monaco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angela","family":"Lombardi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cataldo","family":"Guaragnella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Bellotti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabina","family":"Tangaro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jneumeth.2017.12.011_bib0005","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","article-title":"The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease","volume":"7","author":"Albert","year":"2011","journal-title":"Alzheimers Dement."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0010","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.jalz.2016.02.006","article-title":"Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease","volume":"12","author":"Allen","year":"2016","journal-title":"Alzheimers Dement."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0015","doi-asserted-by":"crossref","first-page":"8851","DOI":"10.1088\/0031-9155\/60\/22\/8851","article-title":"Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool","volume":"60","author":"Amoroso","year":"2015","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0020","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1192\/apt.21.6.362","article-title":"Mini-Mental State Examination (MMSE) for the detection of Alzheimer's disease and other dementias in people with mild cognitive impairment (MCI)","volume":"21","author":"Arevalo-Rodriguez","year":"2015","journal-title":"BJPsych Adv."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0025","series-title":"Understanding Deep Neural Networks with Rectified Linear Units","author":"Arora","year":"2016"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0030","first-page":"153","article-title":"Greedy layer-wise training of deep networks","volume":"19","author":"Bengio","year":"2007","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0035","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.jneumeth.2017.12.011_bib0040","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"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0045","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1140\/epjp\/i2012-12135-6","article-title":"Alzheimer's disease markers from structural MRI and FDG-PET brain images","volume":"127","author":"Chincarini","year":"2012","journal-title":"Eur. Phys. J. Plus"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0050","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1016\/j.neuroimage.2015.10.065","article-title":"Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease","volume":"125","author":"Chincarini","year":"2016","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0055","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1016\/j.neuroimage.2011.09.085","article-title":"Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data","volume":"59","author":"Cho","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0060","doi-asserted-by":"crossref","first-page":"2322-e19","DOI":"10.1016\/j.neurobiolaging.2010.05.023","article-title":"Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification","volume":"32","author":"Davatzikos","year":"2011","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0065","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1016\/j.neuroimage.2012.01.075","article-title":"MRI hippocampal and entorhinal cortex mapping in predicting conversion to Alzheimer's disease","volume":"60","author":"Devanand","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0070","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/S1474-4422(04)00710-0","article-title":"Amnestic MCI or prodromal Alzheimer's disease?","volume":"3","author":"Dubois","year":"2004","journal-title":"Lancet Neurol."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0075","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0080","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1016\/j.neuroimage.2010.12.066","article-title":"Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI)","volume":"55","author":"Filipovych","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0085","series-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0090","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.neuroimage.2010.10.081","article-title":"Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population","volume":"55","author":"Hinrichs","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0095","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0100","series-title":"Adam: A Method for Stochastic Optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0105","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","article-title":"Large scale deep learning for computer aided detection of mammographic lesions","volume":"35","author":"Kooi","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0110","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1016\/j.neuroimage.2010.03.018","article-title":"Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease","volume":"51","author":"Leung","year":"2010","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0115","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","article-title":"Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease","volume":"62","author":"Liu","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0120","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.jneumeth.2017.12.011_bib0125","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.patrec.2016.10.010","article-title":"Combining multiple approaches for the early diagnosis of Alzheimer's disease","volume":"84","author":"Nanni","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0130","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0135","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1016\/j.ejmp.2014.06.044","article-title":"Automated voxel-by-voxel tissue classification for hippocampal segmentation: methods and validation","volume":"30","author":"Tangaro","year":"2014","journal-title":"Phys. Med."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0140","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.ejmp.2017.04.027","article-title":"A fuzzy-based system reveals Alzheimer's disease onset in subjects with mild cognitive impairment","volume":"38","author":"Tangaro","year":"2017","journal-title":"Phys. Med."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0145","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1016\/j.neurobiolaging.2008.08.013","article-title":"Multi-modal imaging predicts memory performance in normal aging and cognitive decline","volume":"31","author":"Walhovd","year":"2010","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0150","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.nicl.2013.05.004","article-title":"Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment","volume":"2","author":"Young","year":"2013","journal-title":"Neuroimage Clin."},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0155","series-title":"Logic, Thought and Action","first-page":"507","article-title":"From computing with numbers to computing with words-from manipulation of measurements to manipulation of perceptions","author":"Zadeh","year":"1999"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0160","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","article-title":"Multimodal classification of Alzheimer's disease and mild cognitive impairment","volume":"55","author":"Zhang","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0165","doi-asserted-by":"crossref","first-page":"e33182","DOI":"10.1371\/journal.pone.0033182","article-title":"Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers","volume":"7","author":"Zhang","year":"2012","journal-title":"PLoS ONE"},{"key":"10.1016\/j.jneumeth.2017.12.011_bib0170","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s40708-015-0028-9","article-title":"Quantitative multimodal multiparametric imaging in Alzheimer's disease","volume":"3","author":"Zhao","year":"2016","journal-title":"Brain Inform."}],"container-title":["Journal of Neuroscience Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165027017304296?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165027017304296?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:44:46Z","timestamp":1759020286000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0165027017304296"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5]]},"references-count":34,"alternative-id":["S0165027017304296"],"URL":"https:\/\/doi.org\/10.1016\/j.jneumeth.2017.12.011","relation":{},"ISSN":["0165-0270"],"issn-type":[{"value":"0165-0270","type":"print"}],"subject":[],"published":{"date-parts":[[2018,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge","name":"articletitle","label":"Article Title"},{"value":"Journal of Neuroscience Methods","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jneumeth.2017.12.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2017 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}