{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T20:40:38Z","timestamp":1781383238202,"version":"3.54.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T00:00:00Z","timestamp":1557964800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T00:00:00Z","timestamp":1557964800000},"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":["Neuroinform"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1007\/s12021-019-09419-w","type":"journal-article","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T05:26:10Z","timestamp":1557984370000},"page":"71-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":178,"title":["3D-Deep Learning Based Automatic Diagnosis of Alzheimer\u2019s Disease with Joint MMSE Prediction Using Resting-State fMRI"],"prefix":"10.1007","volume":"18","author":[{"given":"Nguyen Thanh","family":"Duc","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seungjun","family":"Ryu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Naveed Iqbal","family":"Qureshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun Ho","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7233-5833","authenticated-orcid":false,"given":"Boreom","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"issue":"8","key":"9419_CR1","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1002\/hbm.20929","volume":"31","author":"A Abou-Elseoud","year":"2010","unstructured":"Abou-Elseoud, A., Starck, T., Remes, J., Nikkinen, J., Tervonen, O., & Kiviniemi, V. (2010). The effect of model order selection in group PICA. Human Brain Mapping, 31(8), 1207\u20131216.","journal-title":"Human Brain Mapping"},{"issue":"1","key":"9419_CR2","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.neuroimage.2010.05.067","volume":"53","author":"M Assaf","year":"2010","unstructured":"Assaf, M., Jagannathan, K., Calhoun, V. D., Miller, L., Stevens, M. C., Sahl, R., O'Boyle, J. G., Schultz, R. T., & Pearlson, G. D. (2010). Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. Neuroimage, 53(1), 247\u2013256.","journal-title":"Neuroimage"},{"issue":"5","key":"9419_CR3","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/S1474-4422(11)70077-1","volume":"10","author":"H Barthel","year":"2011","unstructured":"Barthel, H., Gertz, H. J., Dresel, S., Peters, O., Bartenstein, P., Buerger, K., Hiemeyer, F., Wittemer-Rump, S. M., Seibyl, J., Reininger, C., Sabri, O., & Florbetaben Study Group. (2011). Cerebral amyloid-\u03b2 PET with florbetaben (18F) in patients with Alzheimer's disease and healthy controls: a multicentre phase 2 diagnostic study. The Lancet Neurology, 10(5), 424\u2013435.","journal-title":"The Lancet Neurology"},{"issue":"1","key":"9419_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1076\/anec.11.1.1.29366","volume":"11","author":"SRD Beaman","year":"2004","unstructured":"Beaman, S. R. D., Beaman, P. E., Garcia-Pena, C., Villa, M. A., Heres, J., C\u00f3rdova, A., & Jagger, C. (2004). Validation of a modified version of the Mini-Mental State Examination (MMSE) in Spanish. Aging, Neuropsychology, and Cognition, 11(1), 1\u201311.","journal-title":"Aging, Neuropsychology, and Cognition"},{"issue":"Suppl 1","key":"9419_CR5","doi-asserted-by":"crossref","first-page":"S148","DOI":"10.1016\/S1053-8119(09)71511-3","volume":"47","author":"CF Beckmann","year":"2009","unstructured":"Beckmann, C. F., Mackay, C. E., Filippini, N., & Smith, S. M. (2009). Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. NeuroImage, 47(Suppl 1), S148.","journal-title":"NeuroImage"},{"key":"9419_CR6","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.neuroimage.2013.05.099","volume":"83","author":"RM Birn","year":"2013","unstructured":"Birn, R. M., Molloy, E. K., Patriat, R., Parker, T., Meier, T. B., Kirk, G. R., Nair, V. A., Meyerand, M. E., & Prabhakaran, V. (2013). The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage, 83, 550\u2013558.","journal-title":"NeuroImage"},{"issue":"1","key":"9419_CR7","first-page":"22.2","volume":"41","author":"DE Bloom","year":"2011","unstructured":"Bloom, D. E., Boersch-Supan, A., McGee, P., & Seike, A. (2011). Population aging: facts, challenges, and responses. Benefits and Compensation International, 41(1), 22.2.","journal-title":"Benefits and Compensation International"},{"issue":"26","key":"9419_CR8","doi-asserted-by":"crossref","first-page":"8890","DOI":"10.1523\/JNEUROSCI.5698-11.2012","volume":"32","author":"MR Brier","year":"2012","unstructured":"Brier, M. R., Thomas, J. B., Snyder, A. Z., Benzinger, T. L., Zhang, D., Raichle, M. E., Holtzman, D. M., Morris, J. C., & Ances, B. M. (2012). Loss of intranetwork and internetwork resting state functional connections with Alzheimer\u2019s disease progression. Journal of Neuroscience, 32(26), 8890\u20138899.","journal-title":"Journal of Neuroscience"},{"issue":"16","key":"9419_CR9","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1097\/01.wnr.0000239956.45448.4c","volume":"17","author":"VL Cherkassky","year":"2006","unstructured":"Cherkassky, V. L., Kana, R. K., Keller, T. A., & Just, M. A. (2006). Functional connectivity in a baseline resting-state network in autism. Neuroreport, 17(16), 1687\u20131690.","journal-title":"Neuroreport"},{"issue":"9696","key":"9419_CR10","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/S0140-6736(09)61460-4","volume":"374","author":"K Christensen","year":"2009","unstructured":"Christensen, K., Doblhammer, G., Rau, R., & Vaupel, J. W. (2009). Ageing populations: the challenges ahead. The Lancet, 374(9696), 1196\u20131208.","journal-title":"The Lancet"},{"issue":"12","key":"9419_CR11","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1001\/archneur.60.12.1696","volume":"60","author":"CM Clark","year":"2003","unstructured":"Clark, C. M., Xie, S., Chittams, J., Ewbank, D., Peskind, E., Galasko, D., Morris, J. C., McKeel, D. W., Farlow, M., Weitlauf, S. L., Quinn, J., Kaye, J., Knopman, D., Arai, H., Doody, R. S., DeCarli, C., Leight, S., Lee, V. M. Y., & Trojanowski, J. Q. (2003). Cerebrospinal fluid tau and \u03b2-amyloid: how well do these biomarkers reflect autopsy-confirmed dementia diagnoses? Archives of Neurology, 60(12), 1696\u20131702.","journal-title":"Archives of Neurology"},{"issue":"3","key":"9419_CR12","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273\u2013297.","journal-title":"Machine Learning"},{"issue":"2","key":"9419_CR13","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/S1053-8119(03)00049-1","volume":"19","author":"DD Cox","year":"2003","unstructured":"Cox, D. D., & Savoy, R. L. (2003). Functional magnetic resonance imaging (fMRI)\u201cbrain reading\u201d: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19(2), 261\u2013270.","journal-title":"NeuroImage"},{"issue":"26","key":"9419_CR14","doi-asserted-by":"crossref","first-page":"11073","DOI":"10.1073\/pnas.0704320104","volume":"104","author":"NU Dosenbach","year":"2007","unstructured":"Dosenbach, N. U., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A., et al. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences, 104(26), 11073\u201311078.","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"1\u20132","key":"9419_CR15","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s00429-008-0189-x","volume":"213","author":"WC Drevets","year":"2008","unstructured":"Drevets, W. C., Price, J. L., & Furey, M. L. (2008). Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Structure and Function, 213(1\u20132), 93\u2013118.","journal-title":"Brain Structure and Function"},{"key":"9419_CR16","doi-asserted-by":"publisher","first-page":"026033","DOI":"10.1088\/1741-2552\/ab0169","volume":"16","author":"NT Duc","year":"2019","unstructured":"Duc, N. T., & Lee, B. (2019). Microstate functional connectivity in EEG cognitive task revealed by multivariate Gaussian hidden Markov model with phase locking value. Journal of Neural Engineering, 16, 026033. https:\/\/doi.org\/10.1088\/1741-2552\/ab0169 .","journal-title":"Journal of Neural Engineering"},{"key":"9419_CR17","doi-asserted-by":"crossref","unstructured":"Duchesne, S., Caroli, A., Geroldi, C., Frisoni, G.B., & Collins, D.L. (2005). Predicting clinical variable from MRI features: application to MMSE in MCI. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 392\u2013399). Springer, Berlin, Heidelberg.","DOI":"10.1007\/11566465_49"},{"issue":"4","key":"9419_CR18","doi-asserted-by":"crossref","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, D. L., & Frisoni, G. B. (2009). Relating one-year cognitive change in mild cognitive impairment to baseline MRI features. Neuroimage, 47(4), 1363\u20131370.","journal-title":"Neuroimage"},{"issue":"3","key":"9419_CR19","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0018111","volume":"6","author":"J Dukart","year":"2011","unstructured":"Dukart, J., Mueller, K., Horstmann, A., Barthel, H., M\u00f6ller, H. E., Villringer, A., Sabri, O., & Schroeter, M. L. (2011). Combined evaluation of FDG-PET and MRI improves detection and differentiation of dementia. PLoS One, 6(3), e18111.","journal-title":"PLoS One"},{"key":"9419_CR20","doi-asserted-by":"publisher","unstructured":"Fan, Y., Kaufer, D., & Shen, D. (2010). Joint estimation of multiple clinical variables of neurological diseases from imaging patterns. In Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on 852\u2013855. https:\/\/doi.org\/10.1109\/ISBI.2010.5490120 .","DOI":"10.1109\/ISBI.2010.5490120"},{"key":"9419_CR21","doi-asserted-by":"crossref","first-page":"S121","DOI":"10.1097\/00002826-199501001-00014","volume":"18","author":"L Farde","year":"1995","unstructured":"Farde, L., Nordstr\u00f6m, A. L., Karlsson, P., Halldin, C., & Sedvall, G. (1995). Positron emission tomography studies on dopamine receptors in schizophrenia. Clinical Neuropharmacology, 18, S121\u2013S129.","journal-title":"Clinical Neuropharmacology"},{"issue":"38","key":"9419_CR22","first-page":"29","volume":"2","author":"M Foroughan","year":"2008","unstructured":"Foroughan, M., Jafari, Z., Shirin, B. P., Ghaem, M. F. Z., & Rahgozar, M. (2008). Validation of mini-mental state examination (MMSE) in the elderly population of Tehran. Advances in Cognitive Science, 2(38), 29\u201337.","journal-title":"Advances in Cognitive Science"},{"issue":"6","key":"9419_CR23","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1177\/153331750001500604","volume":"15","author":"KN Fountoulakis","year":"2000","unstructured":"Fountoulakis, K. N., Tsolaki, M., Chantzi, H., & Kazis, A. (2000). Mini mental state examination (MMSE): a validation study in Greece. American Journal of Alzheimer\u2019s Disease, 15(6), 342\u2013345.","journal-title":"American Journal of Alzheimer\u2019s Disease"},{"issue":"26","key":"9419_CR24","doi-asserted-by":"crossref","first-page":"10046","DOI":"10.1073\/pnas.0604187103","volume":"103","author":"MD Fox","year":"2006","unstructured":"Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2006). Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences, 103(26), 10046\u201310051.","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"4","key":"9419_CR25","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1016\/j.neurobiolaging.2012.09.015","volume":"34","author":"R Franciotti","year":"2013","unstructured":"Franciotti, R., Falasca, N. W., Bonanni, L., Anzellotti, F., Maruotti, V., Comani, S., Thomas, A., Tartaro, A., Taylor, J. P., & Onofrj, M. (2013). Default network is not hypoactive in dementia with fluctuating cognition: an Alzheimer disease\/dementia with Lewy bodies comparison. Neurobiology of Aging, 34(4), 1148\u20131158.","journal-title":"Neurobiology of Aging"},{"issue":"1","key":"9419_CR26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1\u201322.","journal-title":"Journal of Statistical Software"},{"issue":"2","key":"9419_CR27","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/nrneurol.2009.215","volume":"6","author":"GB Frisoni","year":"2010","unstructured":"Frisoni, G. B., Fox, N. C., Jack, C. R., Jr., Scheltens, P., & Thompson, P. M. (2010). The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology, 6(2), 67\u201377.","journal-title":"Nature Reviews Neurology"},{"issue":"5","key":"9419_CR28","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.biopsych.2006.09.020","volume":"62","author":"MD Greicius","year":"2007","unstructured":"Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., Reiss, A. L., & Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry, 62(5), 429\u2013437.","journal-title":"Biological Psychiatry"},{"issue":"1\u20133","key":"9419_CR29","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1\u20133), 389\u2013422.","journal-title":"Machine Learning"},{"key":"9419_CR30","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE International Conference on Computer Vision 1026\u20131034. https:\/\/doi.org\/10.1109\/ICCV.2015.123 .","DOI":"10.1109\/ICCV.2015.123"},{"issue":"21","key":"9419_CR31","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1212\/WNL.0b013e3181c34b47","volume":"73","author":"S Hoops","year":"2009","unstructured":"Hoops, S., Nazem, S., Siderowf, A. D., Duda, J. E., Xie, S. X., Stern, M. B., & Weintraub, D. (2009). Validity of the MoCA and MMSE in the detection of MCI and dementia in Parkinson disease. Neurology, 73(21), 1738\u20131745.","journal-title":"Neurology"},{"issue":"1","key":"9419_CR32","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.mri.2011.07.007","volume":"30","author":"M Jin","year":"2012","unstructured":"Jin, M., Pelak, V. S., & Cordes, D. (2012). Aberrant default mode network in subjects with amnestic mild cognitive impairment using resting-state functional MRI. Magnetic Resonance Imaging, 30(1), 48\u201361.","journal-title":"Magnetic Resonance Imaging"},{"key":"9419_CR33","unstructured":"Kinsella, K., & Phillips, D. R. (2005). Global aging: The challenge of success, Population Reference Bureau. Washington, DC."},{"issue":"10","key":"9419_CR34","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.4065\/78.10.1290","volume":"78","author":"David S. Knopman","year":"2003","unstructured":"Knopman, D. S., Boeve, B. F., & Petersen, R. C. (2003). Essentials of the proper diagnoses of mild cognitive impairment, dementia, and major subtypes of dementia. In Mayo Clinic Proceedings 78 (10), 1290\u20131308. https:\/\/doi.org\/10.4065\/78.10.1290 .","journal-title":"Mayo Clinic Proceedings"},{"issue":"3","key":"9419_CR35","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.neurobiolaging.2010.04.013","volume":"33","author":"W Koch","year":"2012","unstructured":"Koch, W., Teipel, S., Mueller, S., Benninghoff, J., Wagner, M., Bokde, A. L., et al. (2012). Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer\u2019s disease. Neurobiology of Aging, 33(3), 466\u2013478.","journal-title":"Neurobiology of Aging"},{"issue":"2","key":"9419_CR36","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.neuroimage.2005.01.048","volume":"26","author":"S LaConte","year":"2005","unstructured":"LaConte, S., Strother, S., Cherkassky, V., Anderson, J., & Hu, X. (2005). Support vector machines for temporal classification of block design fMRI data. NeuroImage, 26(2), 317\u2013329.","journal-title":"NeuroImage"},{"issue":"4","key":"9419_CR37","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","volume":"62","author":"S Liu","year":"2015","unstructured":"Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI. (2015). Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer\u2019s disease. IEEE Transactions on Biomedical Engineering, 62(4), 1132\u20131140.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"16\u201318","key":"9419_CR38","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.1016\/j.neucom.2005.06.021","volume":"69","author":"W Lu","year":"2006","unstructured":"Lu, W., & Rajapakse, J. C. (2006). ICA with reference. Neurocomputing, 69(16\u201318), 2244\u20132257.","journal-title":"Neurocomputing"},{"key":"9419_CR39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2012\/961257","volume":"2012","author":"A Mahmoudi","year":"2012","unstructured":"Mahmoudi, A., Takerkart, S., Regragui, F., Boussaoud, D., & Brovelli, A. (2012). Multivoxel pattern analysis for FMRI data: a review. Computational and Mathematical Methods in Medicine, 2012, 1\u201314.","journal-title":"Computational and Mathematical Methods in Medicine"},{"issue":"4","key":"9419_CR40","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1016\/j.neuroimage.2005.06.070","volume":"28","author":"J Mourao-Miranda","year":"2005","unstructured":"Mourao-Miranda, J., Bokde, A. L., Born, C., Hampel, H., & Stetter, M. (2005). Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage, 28(4), 980\u2013995.","journal-title":"NeuroImage"},{"key":"9419_CR41","doi-asserted-by":"publisher","first-page":"e0212582","DOI":"10.1371\/journal.pone.0212582","volume":"14","author":"DT Nguyen","year":"2019","unstructured":"Nguyen, D. T., Ryu, S., Qureshi, M. N. I., Choi, M., Lee, K. H., & Lee, B. (2019). Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer\u2019s dementia diagnosis using multi-measure rs-fMRI spatial patterns. PLOS One, 14, e0212582. https:\/\/doi.org\/10.1371\/journal.pone.0212582 .","journal-title":"PLOS One"},{"issue":"1","key":"9419_CR42","doi-asserted-by":"crossref","first-page":"e00602","DOI":"10.1002\/brb3.602","volume":"7","author":"J Oh","year":"2017","unstructured":"Oh, J., Chun, J. W., Kim, E., Park, H. J., Lee, B., & Kim, J. J. (2017). Aberrant neural networks for the recognition memory of socially relevant information in patients with schizophrenia. Brain and Behavior, 7(1), e00602.","journal-title":"Brain and Behavior"},{"issue":"6617","key":"9419_CR43","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1038\/385634a0","volume":"385","author":"Y Okubo","year":"1997","unstructured":"Okubo, Y., Suhara, T., Suzuki, K., Kobayashi, K., Inoue, O., Terasaki, O., Someya, Y., Sassa, T., Sudo, Y., Matsushima, E., Iyo, M., Tateno, Y., & Toru, M. (1997). Decreased prefrontal dopamine D1 receptors in schizophrenia revealed by PET. Nature, 385(6617), 634\u2013636.","journal-title":"Nature"},{"issue":"6","key":"9419_CR44","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s00259-018-3933-3","volume":"45","author":"TA Pascoal","year":"2018","unstructured":"Pascoal, T. A., Mathotaarachchi, S., Shin, M., Park, A. Y., Mohades, S., Benedet, A. L., et al. (2018). Amyloid and tau signatures of brain metabolic decline in preclinical Alzheimer\u2019s disease. European Journal of Nuclear Medicine and Molecular Imaging, 45(6), 1021\u20131030.","journal-title":"European Journal of Nuclear Medicine and Molecular Imaging"},{"key":"9419_CR45","volume-title":"World Alzheimer report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future","author":"M Prince","year":"2016","unstructured":"Prince, M., Comas-Herrera, A., Knapp, M., Guerchet, M., & Karagiannidou, M. (2016). World Alzheimer report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future. Alzheimer\u2019s Disease International."},{"issue":"8","key":"9419_CR46","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1002\/hbm.20444","volume":"29","author":"A Qiu","year":"2008","unstructured":"Qiu, A., Vaillant, M., Barta, P., Ratnanather, J. T., & Miller, M. I. (2008). Region-of-interest-based analysis with application of cortical thickness variation of left planum temporale in schizophrenia and psychotic bipolar disorder. Human Brain Mapping, 29(8), 973\u2013985.","journal-title":"Human Brain Mapping"},{"key":"9419_CR47","doi-asserted-by":"crossref","first-page":"e0160697","DOI":"10.1371\/journal.pone.0160697","volume":"11","author":"MNI Qureshi","year":"2016","unstructured":"Qureshi, M. N. I., Min, B., Jo, H. J., & Lee, B. (2016). Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: structural MRI study. PLoS One, 11, e0160697.","journal-title":"PLoS One"},{"key":"9419_CR48","doi-asserted-by":"crossref","first-page":"59","DOI":"10.3389\/fninf.2017.00059","volume":"11","author":"MNI Qureshi","year":"2017","unstructured":"Qureshi, M. N. I., Oh, J., Cho, D., Jo, H. J., & Lee, B. (2017a). Multimodal discrimination of schizophrenia using hybrid weighted feature concatenation of brain functional connectivity and anatomical features with an extreme learning machine. Frontiers in Neuroinformatics, 11, 59.","journal-title":"Frontiers in Neuroinformatics"},{"key":"9419_CR49","doi-asserted-by":"publisher","unstructured":"Qureshi, M. N. I., Oh, J., Min, B., Jo, H. J., & Lee, B. (2017b). Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain MRI. Frontiers in Human Neuroscience. https:\/\/doi.org\/10.3389\/fnhum.2017.00157 .","DOI":"10.3389\/fnhum.2017.00157"},{"key":"9419_CR50","doi-asserted-by":"publisher","unstructured":"Qureshi, M. N. I., Ryu, S., Song, J., Lee, K., & Lee, B. (2019). Evaluation of functional decline in Alzheimer\u2019s dementia using 3D deep learning and group ICA for rs-fMRI measurements. Front Aging Neuroscience. https:\/\/doi.org\/10.3389\/fnagi.2019.00008 .","DOI":"10.3389\/fnagi.2019.00008"},{"issue":"3","key":"9419_CR51","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.neuroimage.2007.06.003","volume":"37","author":"JC Rajapakse","year":"2007","unstructured":"Rajapakse, J. C., & Zhou, J. (2007). Learning effective brain connectivity with dynamic Bayesian networks. NeuroImage, 37(3), 749\u2013760.","journal-title":"NeuroImage"},{"key":"9419_CR52","doi-asserted-by":"crossref","first-page":"897","DOI":"10.3389\/fnhum.2014.00897","volume":"8","author":"B Rashid","year":"2014","unstructured":"Rashid, B., Damaraju, E., Pearlson, G. D., & Calhoun, V. D. (2014). Dynamic connectivity states estimated from resting fMRI identify differences among schizophrenia, bipolar disorder, and healthy control subjects. Frontiers in Human Neuroscience, 8, 897.","journal-title":"Frontiers in Human Neuroscience"},{"issue":"2","key":"9419_CR53","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1377\/hlthaff.12.2.164","volume":"12","author":"DP Rice","year":"1993","unstructured":"Rice, D. P., Fox, P. J., Max, W., Webber, P. A., Hauck, W. W., Lindeman, D. A., & Segura, E. (1993). The economic burden of Alzheimer\u2019s disease care. Health Affairs, 12(2), 164\u2013176.","journal-title":"Health Affairs"},{"issue":"4","key":"9419_CR54","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1002\/hbm.20160","volume":"26","author":"SA Rombouts","year":"2005","unstructured":"Rombouts, S. A., Barkhof, F., Goekoop, R., Stam, C. J., & Scheltens, P. (2005). Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: an fMRI study. Human Brain Mapping, 26(4), 231\u2013239.","journal-title":"Human Brain Mapping"},{"issue":"6553","key":"9419_CR55","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1038\/378176a0","volume":"378","author":"DA Silbersweig","year":"1995","unstructured":"Silbersweig, D. A., Stern, E., Frith, C., Cahill, C., Holmes, A., Grootoonk, S., Seaward, J., McKenna, P., Chua, S. E., Schnorr, L., Jones, T., & Frackowiak, R. S. J. (1995). A functional neuroanatomy of hallucinations in schizophrenia. Nature, 378(6553), 176\u2013179.","journal-title":"Nature"},{"key":"9419_CR56","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv [preprint]:1409.1556."},{"key":"9419_CR57","doi-asserted-by":"crossref","first-page":"S208","DOI":"10.1016\/j.neuroimage.2004.07.051","volume":"23","author":"SM Smith","year":"2004","unstructured":"Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208\u2013S219.","journal-title":"NeuroImage"},{"issue":"4","key":"9419_CR58","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1016\/j.neuroimage.2010.03.051","volume":"51","author":"CM Stonnington","year":"2010","unstructured":"Stonnington, C. M., Chu, C., Kl\u00f6ppel, S., Jack, C. R., Jr., Ashburner, J., Frackowiak, R. S., & Alzheimer Disease Neuroimaging Initiative. (2010). Predicting clinical scores from magnetic resonance scans in Alzheimer\u2019s disease. NeuroImage, 51(4), 1405\u20131413.","journal-title":"NeuroImage"},{"key":"9419_CR59","doi-asserted-by":"crossref","unstructured":"Suk, H. I., & Shen, D. (2013). Deep learning-based feature representation for AD\/MCI classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 583\u2013590). Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-642-40763-5_72"},{"issue":"2","key":"9419_CR60","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s00429-013-0687-3","volume":"220","author":"HI Suk","year":"2015","unstructured":"Suk, H. I., Lee, S. W., Shen, D., & Alzheimer\u2019s Disease Neuroimaging Initiative. (2015). Latent feature representation with stacked auto-encoder for AD\/MCI diagnosis. Brain Structure and Function, 220(2), 841\u2013859.","journal-title":"Brain Structure and Function"},{"key":"9419_CR61","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.neuroimage.2016.01.005","volume":"129","author":"HI Suk","year":"2016","unstructured":"Suk, H. I., Wee, C. Y., Lee, S. W., & Shen, D. (2016). State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage, 129, 292\u2013307.","journal-title":"NeuroImage"},{"issue":"6","key":"9419_CR62","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1000100","volume":"4","author":"K Supekar","year":"2008","unstructured":"Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS Computational Biology, 4(6), e1000100.","journal-title":"PLoS Computational Biology"},{"issue":"7","key":"9419_CR63","doi-asserted-by":"crossref","first-page":"e1000157","DOI":"10.1371\/journal.pbio.1000157","volume":"7","author":"K Supekar","year":"2009","unstructured":"Supekar, K., Musen, M., & Menon, V. (2009). Development of large-scale functional brain networks in children. PLoS Biology, 7(7), e1000157.","journal-title":"PLoS Biology"},{"issue":"7","key":"9419_CR64","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s40263-015-0258-7","volume":"29","author":"YY Syed","year":"2015","unstructured":"Syed, Y. Y., & Deeks, E. (2015). [18F] Florbetaben: a review in \u03b2-amyloid PET imaging in cognitive impairment. CNS Drugs, 29(7), 605\u2013613.","journal-title":"CNS Drugs"},{"issue":"34","key":"9419_CR65","doi-asserted-by":"crossref","first-page":"10671","DOI":"10.1523\/JNEUROSCI.1141-09.2009","volume":"29","author":"CM Sylvester","year":"2009","unstructured":"Sylvester, C. M., Shulman, G. L., Jack, A. I., & Corbetta, M. (2009). Anticipatory and stimulus-evoked blood oxygenation level-dependent modulations related to spatial attention reflect a common additive signal. Journal of Neuroscience, 29(34), 10671\u201310682.","journal-title":"Journal of Neuroscience"},{"issue":"1","key":"9419_CR66","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"Robert Tibshirani","year":"1996","unstructured":"Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267\u2013288.","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"1","key":"9419_CR67","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neuroimage.2011.07.086","volume":"59","author":"PC Tu","year":"2012","unstructured":"Tu, P. C., Hsieh, J. C., Li, C. T., Bai, Y. M., & Su, T. P. (2012). Cortico-striatal disconnection within the cingulo-opercular network in schizophrenia revealed by intrinsic functional connectivity analysis: a resting fMRI study. NeuroImage, 59(1), 238\u2013247.","journal-title":"NeuroImage"},{"issue":"6","key":"9419_CR68","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s004150050387","volume":"246","author":"PJ Visser","year":"1999","unstructured":"Visser, P. J., Scheltens, P., Verhey, F. R., Schmand, B., Launer, L. J., Jolles, J., & Jonker, C. (1999). Medial temporal lobe atrophy and memory dysfunction as predictors for dementia in subjects with mild cognitive impairment. Journal of Neurology, 246(6), 477\u2013485.","journal-title":"Journal of Neurology"},{"issue":"2","key":"9419_CR69","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.neuroimage.2005.12.033","volume":"31","author":"L Wang","year":"2006","unstructured":"Wang, L., Zang, Y., He, Y., Liang, M., Zhang, X., Tian, L., Wu, T., Jiang, T., & Li, K. (2006). Changes in hippocampal connectivity in the early stages of Alzheimer\u2019s disease: evidence from resting state fMRI. NeuroImage, 31(2), 496\u2013504.","journal-title":"NeuroImage"},{"issue":"4","key":"9419_CR70","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1016\/j.neuroimage.2007.03.072","volume":"36","author":"Z Wang","year":"2007","unstructured":"Wang, Z., Childress, A. R., Wang, J., & Detre, J. A. (2007). Support vector machine learning-based fMRI data group analysis. NeuroImage, 36(4), 1139\u20131151.","journal-title":"NeuroImage"},{"issue":"4","key":"9419_CR71","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1016\/j.neuroimage.2009.12.092","volume":"50","author":"Y Wang","year":"2010","unstructured":"Wang, Y., Fan, Y., Bhatt, P., & Davatzikos, C. (2010a). High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. NeuroImage, 50(4), 1519\u20131535.","journal-title":"NeuroImage"},{"key":"9419_CR72","first-page":"16","volume":"4","author":"J Wang","year":"2010","unstructured":"Wang, J., Zuo, X., & He, Y. (2010b). Graph-based network analysis of resting-state functional MRI. Frontiers in Systems Neuroscience, 4, 16.","journal-title":"Frontiers in Systems Neuroscience"},{"key":"9419_CR73","doi-asserted-by":"crossref","unstructured":"Wang, H., Nie, F., Huang, H., Risacher, S., Saykin, A. J., & Shen, L. (2011). Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 115\u2013123). Springer, Berlin, Heidelberg.","DOI":"10.1007\/978-3-642-23626-6_15"},{"issue":"1","key":"9419_CR74","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.neuroimage.2011.07.079","volume":"59","author":"XF Wang","year":"2012","unstructured":"Wang, X. F., Jiang, Z., Daly, J. J., & Yue, G. H. (2012). A generalized regression model for region of interest analysis of fMRI data. NeuroImage, 59(1), 502\u2013510.","journal-title":"NeuroImage"},{"issue":"6","key":"9419_CR75","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1093\/bioinformatics\/btp041","volume":"25","author":"TT Wu","year":"2009","unstructured":"Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., & Lange, K. (2009). Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics, 25(6), 714\u2013721.","journal-title":"Bioinformatics"},{"key":"9419_CR76","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.snb.2015.02.025","volume":"212","author":"K Yan","year":"2015","unstructured":"Yan, K., & Zhang, D. (2015). Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212, 353\u2013363.","journal-title":"Sensors and Actuators B: Chemical"},{"issue":"2","key":"9419_CR77","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.neuroimage.2011.09.069","volume":"59","author":"D Zhang","year":"2012","unstructured":"Zhang, D., Shen, D., & Alzheimer's Disease Neuroimaging Initiative. (2012). Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. NeuroImage, 59(2), 895\u2013907.","journal-title":"NeuroImage"},{"issue":"12","key":"9419_CR78","doi-asserted-by":"crossref","first-page":"5861","DOI":"10.1002\/hbm.22590","volume":"35","author":"Y Zhang","year":"2014","unstructured":"Zhang, Y., Kimberg, D. Y., Coslett, H. B., Schwartz, M. F., & Wang, Z. (2014). Multivariate lesion-symptom mapping using support vector regression. Human Brain Mapping, 35(12), 5861\u20135876.","journal-title":"Human Brain Mapping"},{"key":"9419_CR79","doi-asserted-by":"crossref","unstructured":"Zhu, X., Suk, H. I., & Shen, D. (2014). A novel multi-relation regularization method for regression and classification in AD diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 401\u2013408). Springer, Cham.","DOI":"10.1007\/978-3-319-10443-0_51"},{"key":"9419_CR80","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.media.2015.10.008","volume":"38","author":"X Zhu","year":"2017","unstructured":"Zhu, X., Suk, H. I., Wang, L., Lee, S. W., Shen, D., & Alzheimer\u2019s Disease Neuroimaging Initiative. (2017). A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Medical Image Analysis, 38, 205\u2013214.","journal-title":"Medical Image Analysis"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-019-09419-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12021-019-09419-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-019-09419-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T02:55:02Z","timestamp":1721271302000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12021-019-09419-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,16]]},"references-count":80,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["9419"],"URL":"https:\/\/doi.org\/10.1007\/s12021-019-09419-w","relation":{},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"value":"1539-2791","type":"print"},{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,16]]},"assertion":[{"value":"16 May 2019","order":1,"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":"none.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of Interest"}}]}}