{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T12:34:14Z","timestamp":1777811654745,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s12559-019-09708-1","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T08:02:47Z","timestamp":1582185767000},"page":"34-48","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Computer-Aided Dementia Diagnosis Based on Hierarchical Extreme Learning Machine"],"prefix":"10.1007","volume":"13","author":[{"given":"Zhongyang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchang","family":"Xin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huizi","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"key":"9708_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jalz.2016.07.150","volume":"13","author":"A Wimo","year":"2017","unstructured":"Wimo A, et al. The worldwide costs of dementia 2015 and comparisons with 2010. Alzheimers Dement 2017; 13:1\u20137.","journal-title":"Alzheimers Dement"},{"key":"9708_CR2","doi-asserted-by":"publisher","first-page":"157","DOI":"10.2174\/1567205014666171030115624","volume":"15","author":"S Misiewicz","year":"2018","unstructured":"Misiewicz S, Brickman AM, Tosto G. Prosodic impairment in dementia: review of the literature. Curr Alzheimer Res 2018;15:157\u201363.","journal-title":"Curr Alzheimer Res"},{"key":"9708_CR3","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s12559-018-9614-5","volume":"11","author":"F Shi","year":"2018","unstructured":"Shi F, et al. Meta-KANSEI modeling with valence-arousal fMRI dataset of brain. Cogn Comput 2018;11:227\u201340.","journal-title":"Cogn Comput"},{"key":"9708_CR4","doi-asserted-by":"publisher","first-page":"20170910","DOI":"10.1259\/bjr.20170910","volume":"91","author":"JM Makaronidis","year":"2018","unstructured":"Makaronidis JM, Batterham RL. Obesity, body weight regulation The brain Insights from fMRI. Br J Radiol 2018;91:20170910.","journal-title":"Br J Radiol"},{"key":"9708_CR5","doi-asserted-by":"publisher","unstructured":"Erica NG, Lara AR. 2019. The use of functional magnetic resonance imaging (fMRI) to test pharmacotherapies for alcohol use disorders: a systematic review Alcoholism (NY). https:\/\/doi.org\/10.1111\/acer.14167.","DOI":"10.1111\/acer.14167"},{"key":"9708_CR6","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.neuroimage.2017.03.057","volume":"155","author":"S Rathore","year":"2017","unstructured":"Rathore S, Habes M, Aksam IM, Shacklett A, Davatzikos C. A review on neuroimaging-based classification sstudies and associated feature extraction methods for Alzheimer\u2019s disease and its prodromal stages. Neuroimage 2017;155:530\u201348.","journal-title":"Neuroimage"},{"key":"9708_CR7","doi-asserted-by":"publisher","unstructured":"Shang J, Fisher P, Daamen M, Josef GB. 2019. A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm Hum Brain Mapp. https:\/\/doi.org\/10.1002\/hbm.24698.","DOI":"10.1002\/hbm.24698"},{"key":"9708_CR8","doi-asserted-by":"publisher","unstructured":"Mcdonough IM, Letang SK, Stinson EA. 2019. Dementia risk elevates brain activity during memory retrieval: a functional MRI analysis of middle aged and older adults. J Alzheimers Dis. https:\/\/doi.org\/10.3233\/JAD-190035.","DOI":"10.3233\/JAD-190035"},{"key":"9708_CR9","doi-asserted-by":"publisher","unstructured":"Li W, Zhao Y, Xiao Y, Chen X. 2018. Detecting Alzheimer\u2019s disease on small dataset: a knowledge transfer perspective. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2018.2839771.","DOI":"10.1109\/JBHI.2018.2839771"},{"key":"9708_CR10","doi-asserted-by":"publisher","first-page":"1776","DOI":"10.3174\/ajnr.A5543","volume":"39","author":"G Zaharchuk","year":"2018","unstructured":"Zaharchuk G, Gong E, Wintermark M. Rubind, Langlotz P. Deep learning in neuroradiology. Am J Neuroradiol 2018;39:1776\u201384.","journal-title":"Am J Neuroradiol"},{"key":"9708_CR11","first-page":"816","volume-title":"Deep learning-based pipeline to recognize Alzheimer\u2019s disease using fMRI data. Proceedings of 2016 future technologties conference","author":"S Sarraf","year":"2017","unstructured":"Sarraf S, Tofighi G. Deep learning-based pipeline to recognize Alzheimer\u2019s disease using fMRI data. Proceedings of 2016 future technologties conference. San Francisco: IEEE Press; 2017, pp. 816\u201320."},{"key":"9708_CR12","first-page":"809","volume":"4","author":"J Tang","year":"2017","unstructured":"Tang J, Deng C, Huang GB. Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 2017;4:809\u201321.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9708_CR13","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1007\/s12559-017-9494-0","volume":"9","author":"L Duan","year":"2017","unstructured":"Duan L, et al. Motor Imagery EEG classification based on kernel hierarchical extreme learning machine. Cogn Comput 2017;9:758\u201365.","journal-title":"Cogn Comput"},{"key":"9708_CR14","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/TBME.2010.2082539","volume":"58","author":"F Lotte","year":"2011","unstructured":"Lotte F, Guan C. Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng 2011;58:355\u20132.","journal-title":"IEEE Trans Biomed Eng"},{"key":"9708_CR15","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.eswa.2017.12.015","volume":"96","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, et al. Multi-kernel e learning machine for EEG classification in brain-computer interfaces. Expert Syst Appl 2018;96:302\u201310.","journal-title":"Expert Syst Appl"},{"key":"9708_CR16","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/MC.2016.307","volume":"49","author":"G Atluri","year":"2016","unstructured":"Atluri G, et al. The vrain-network paradigm: using functional imaging data to study how the brain works. Computer 2016;49:65\u201371.","journal-title":"Computer"},{"key":"9708_CR17","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1007\/s10548-019-00719-7","volume":"32","author":"YB Lee","year":"2019","unstructured":"Lee YB, Yoo K, Roh JH, Moon WJ, Jeong Y. Brain-state extraction algorithm based on the state transition (best): a dynamic functional brain network analysis in fmri study. Brain Topogr 2019;32:897\u201313.","journal-title":"Brain Topogr"},{"key":"9708_CR18","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1007\/s12559-018-9585-6","volume":"10","author":"X Li","year":"2018","unstructured":"Li X, Hu Z, Wang H. Combining non-negative matrix factorization and sparse coding for functional brain overlapping community detection. Cogn Comput 2018;10:991\u201305.","journal-title":"Cogn Comput"},{"key":"9708_CR19","doi-asserted-by":"publisher","unstructured":"Muldoon SF, et al. 2016. Small-world propensity and weighted brain networks. Sci Reports. https:\/\/doi.org\/10.1038\/srep22057.","DOI":"10.1038\/srep22057"},{"key":"9708_CR20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.neulet.2018.03.016","volume":"674","author":"P Li","year":"2018","unstructured":"Li P, et al. Structural and functional brain network of human retrosplenial cortex. Neurosci Lett 2018;674: 24\u20139.","journal-title":"Neurosci Lett"},{"key":"9708_CR21","doi-asserted-by":"publisher","unstructured":"Filippi M, et al. 2018. Changes in functional and structural brain connectome along the Alzheimer\u2019s disease continuum. Mol Psychiatry. https:\/\/doi.org\/10.1038\/s41380-018-0067-8.","DOI":"10.1038\/s41380-018-0067-8"},{"key":"9708_CR22","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1038\/s41593-018-0154-9","volume":"21","author":"S Mostafavi","year":"2018","unstructured":"Mostafavi S, et al. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer\u2019s disease. Nat Neurosci 2018;21:811\u20139.","journal-title":"Nat Neurosci"},{"key":"9708_CR23","doi-asserted-by":"publisher","first-page":"3012","DOI":"10.1093\/brain\/awx224","volume":"140","author":"J Chong","year":"2017","unstructured":"Chong J, et al. Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer\u2019s disease. Brain 2017;140:3012\u201322.","journal-title":"Brain"},{"key":"9708_CR24","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1039\/C6MB00815A","volume":"13","author":"S Sulaimany","year":"2017","unstructured":"Sulaimany S, et al. Predicting brain network changes in Alzheimer\u2019s disease with link prediction algorithms. Mol Biosyst 2017;13:725\u201335.","journal-title":"Mol Biosyst"},{"key":"9708_CR25","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing 2006; 70:489\u201301.","journal-title":"Neurocomputing"},{"key":"9708_CR26","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","volume":"2","author":"GB Huang","year":"2011","unstructured":"Huang GB, Wang DH, Lan Y. Extreme learning machines: a survey. Int J Mach Learn Cybern 2011;2: 107\u201322.","journal-title":"Int J Mach Learn Cybern"},{"key":"9708_CR27","doi-asserted-by":"publisher","first-page":"3056","DOI":"10.1016\/j.neucom.2007.02.009","volume":"70","author":"GB Huang","year":"2007","unstructured":"Huang GB, Chen L. Convex incremental extreme learning machine. Neurocomputing 2007;70:3056\u201362.","journal-title":"Neurocomputing"},{"key":"9708_CR28","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.neucom.2010.02.019","volume":"74","author":"GB Huang","year":"2010","unstructured":"Huang GB, Ding X, Zhou H. Optimization method based extreme learning machine for classification. Neurocomputing 2010;74:155\u201363.","journal-title":"Neurocomputing"},{"key":"9708_CR29","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2012","unstructured":"Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Syst 2012;42:513\u201329.","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"9708_CR30","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/MIS.2013.4","volume":"28","author":"LLC Kasun","year":"2013","unstructured":"Kasun LLC, Zhou H, Huang GB, Vong CM. Representational learning with extreme learning machine for big data. IEEE Intell Syst 2013;28:31\u20134.","journal-title":"IEEE Intell Syst"},{"key":"9708_CR31","first-page":"13","volume":"4","author":"CG Yan","year":"2010","unstructured":"Yan CG, Zane YF. DPARSF A Matlab toolbox for \u2018pipeline\u2019 data analysis of resting-state fMRI. Front in Sys Neurosci 2010;4:13.","journal-title":"Front in Sys Neurosci"},{"key":"9708_CR32","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1016\/j.neuroimage.2004.12.034","volume":"25","author":"SB Eickhoff","year":"2005","unstructured":"Eickhoff SB, et al. A new spm toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 2005;25:1325\u201335.","journal-title":"Neuroimage"},{"key":"9708_CR33","doi-asserted-by":"publisher","unstructured":"Hess AS, Hess JR. 2018. Principal component analysis. Transfusion. https:\/\/doi.org\/10.1111\/trf.14639.","DOI":"10.1111\/trf.14639"},{"key":"9708_CR34","first-page":"27","volume":"3","author":"ZP Liu","year":"2013","unstructured":"Liu ZP. Linear discriminant analysis. Chicago 2013;3:27\u20133.","journal-title":"Chicago"},{"key":"9708_CR35","unstructured":"He X, Niyogi P. Locality preserving projections. NIPS. 2004:153\u2013160."},{"key":"9708_CR36","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1145\/2641190.2641196","volume":"15","author":"X Kong","year":"2014","unstructured":"Kong X, Yu PS. Brain network analysis:a data mining perspective. Acm Sigkdd Explor Newslett 2014;15: 30\u20138.","journal-title":"Acm Sigkdd Explor Newslett"},{"key":"9708_CR37","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzouriomazoyer","year":"2002","unstructured":"Tzouriomazoyer N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273\u201389.","journal-title":"Neuroimage"},{"key":"9708_CR38","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/bs.irn.2017.02.016","volume":"132","author":"N Titova","year":"2018","unstructured":"Titova N, Qamar MA, Chaudhuri KR. The nonmotor features of Parkinson\u2019s disease. Int Rev Neurobiol 2018;132:33\u201354.","journal-title":"Int Rev Neurobiol"},{"key":"9708_CR39","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.bbr.2016.06.043","volume":"322","author":"A Khazaee","year":"2017","unstructured":"Khazaee A, Ebrahimzadeh A, Babajaniferemi A. Classification of patients with MCI And AD from healthy controls using directed graph measures of resting-state fMRI. Behav Brain Res 2017;322:339\u201350.","journal-title":"Behav Brain Res"},{"key":"9708_CR40","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"G Hinton","year":"2006","unstructured":"Hinton G, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science 2006;313: 504\u201307.","journal-title":"Science"},{"key":"9708_CR41","doi-asserted-by":"crossref","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol PA. Extractingand composing robust features with denoising autoencoders. ICML. 2008:1096\u201303.","DOI":"10.1145\/1390156.1390294"},{"key":"9708_CR42","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"G Hinton","year":"2006","unstructured":"Hinton G, Osindero TY. A fast learning algorithm for deep belief nets. Neural Comput 2006;18:1527\u201354.","journal-title":"Neural Comput"},{"key":"9708_CR43","unstructured":"Salakhutdinov R, Hinton G. Deep boltzmann machines. AISTATS. 2009:448\u201355."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-019-09708-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12559-019-09708-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-019-09708-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T20:11:47Z","timestamp":1613765507000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12559-019-09708-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,20]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["9708"],"URL":"https:\/\/doi.org\/10.1007\/s12559-019-09708-1","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,20]]},"assertion":[{"value":"28 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interest"}}]}}