{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T13:43:07Z","timestamp":1776865387298,"version":"3.51.2"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Alz Res Therapy"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract <\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Early and accurate diagnosis of Alzheimer\u2019s disease (AD) is essential for disease management and therapeutic choices that can delay disease progression. Machine learning (ML) approaches have been extensively used in attempts to develop algorithms for reliable early diagnosis of AD, although clinical usefulness, interpretability, and generalizability of the classifiers across datasets and MRI protocols remain limited.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We report a multi-diagnostic and generalizable approach for mild cognitive impairment (MCI) and AD diagnosis using structural MRI and ML. Classifiers were trained and tested using subjects from the AD Neuroimaging Initiative (ADNI) database (<jats:italic>n<\/jats:italic> = 570) and the Open Access Series of Imaging Studies (OASIS) project database (<jats:italic>n<\/jats:italic> = 531). Several classifiers are compared and combined using voting for a decision<jats:italic>.<\/jats:italic> Additionally, we report tests of generalizability across datasets and protocols (IR-SPGR and MPRAGE), the impact of using graph theory measures on diagnostic classification performance, the relative importance of different brain regions on classification for better interpretability, and an evaluation of the potential for clinical applicability of the classifier.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Our \u201chealthy controls (HC) vs. AD\u201d classifier trained and tested on the combination of ADNI and OASIS datasets obtained a balanced accuracy (BAC) of 90.6% and a Matthew\u2019s correlation coefficient (MCC) of 0.811. Our \u201cHC vs. MCI vs. AD\u201d classifier trained and tested on the ADNI dataset obtained a 62.1% BAC (33.3% being the by-chance cut-off) and 0.438 MCC. Hippocampal features were the strongest contributors to the classification decisions (approx. 25\u201345%), followed by temporal (approx. 13%), cingulate, and frontal regions (approx. 8\u201313% each), which is consistent with our current understanding of AD and its progression. Classifiers generalized well across both datasets and protocols. Finally, using graph theory measures did not improve classification performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>In sum, we present a diagnostic tool for MCI and AD trained using baseline scans and a follow-up diagnosis regardless of progression, which is multi-diagnostic, generalizable across independent data sources and acquisition protocols, and with transparently reported performance. Rated as potentially clinically applicable, our tool may be clinically useful to inform diagnostic decisions in dementia, if successful in real-world prospective clinical trials.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13195-022-01047-y","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T10:03:14Z","timestamp":1659520994000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":156,"title":["Early diagnosis of Alzheimer\u2019s disease using machine learning: a multi-diagnostic, generalizable approach"],"prefix":"10.1186","volume":"14","author":[{"given":"Vasco S\u00e1","family":"Diogo","sequence":"first","affiliation":[]},{"given":"Hugo Alexandre","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Diana","family":"Prata","sequence":"additional","affiliation":[]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"1047_CR1","volume-title":"World Alzheimer Report 2015: The Global Impact of Dementia: an analysis of prevalence, incidence, cost and trends","author":"M Prince","year":"2015","unstructured":"Prince M, Wimo A, Guerchet M, Ali G-C, Yu-Tzu W, Prina M. World Alzheimer Report 2015: The Global Impact of Dementia: an analysis of prevalence, incidence, cost and trends. London: Alzheimer\u2019s Disease International (ADI); 2015. [cited 2017 Jun 14]. Available from: https:\/\/www.alz.co.uk\/research\/WorldAlzheimerReport2015.pdf"},{"issue":"1\u20132","key":"1047_CR2","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1159\/000109998","volume":"29","author":"BL Plassman","year":"2007","unstructured":"Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR, Ofstedal MB, et al. Prevalence of dementia in the United States: the aging, demographics, and memory study. Neuroepidemiology. 2007;29(1\u20132):125\u201332.","journal-title":"Neuroepidemiology."},{"issue":"6","key":"1047_CR3","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1016\/S1474-4422(14)70090-0","volume":"13","author":"B Dubois","year":"2014","unstructured":"Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer\u2019s disease: the IWG-2 criteria. Lancet Neurol. 2014;13(6):614\u201329.","journal-title":"Lancet Neurol"},{"issue":"3","key":"1047_CR4","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","volume":"7","author":"MS Albert","year":"2011","unstructured":"Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease. Alzheimers Dement. 2011;7(3):270\u20139.","journal-title":"Alzheimers Dement"},{"issue":"4","key":"1047_CR5","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1016\/j.neuroimage.2008.10.031","volume":"44","author":"C Misra","year":"2009","unstructured":"Misra C, Fan Y, Davatzikos C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI\u2606. NeuroImage. 2009;44(4):1415\u201322.","journal-title":"NeuroImage."},{"key":"#cr-split#-1047_CR6.1","doi-asserted-by":"crossref","unstructured":"Crous-Bou M, Minguill\u00f3n C, Gramunt N, Molinuevo JL. Alzheimer's disease prevention: from risk factors to early intervention. Alzheimers Res Ther. 2017;9","DOI":"10.1186\/s13195-017-0297-z"},{"key":"#cr-split#-1047_CR6.2","unstructured":"(1) [cited 2019 Dec 2]. Available from: http:\/\/alzres.biomedcentral.com\/articles\/10.1186\/s13195-017-0297-z."},{"issue":"2","key":"1047_CR7","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.jalz.2014.06.006","volume":"11","author":"M Bocchetta","year":"2015","unstructured":"Bocchetta M, Galluzzi S, Kehoe PG, Aguera E, Bernabei R, Bullock R, et al. The use of biomarkers for the etiologic diagnosis of MCI in Europe: an EADC survey. Alzheimers Dement. 2015;11(2):195\u2013206.e1.","journal-title":"Alzheimers Dement"},{"key":"1047_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bionps.2019.100005","volume":"1","author":"R Khoury","year":"2019","unstructured":"Khoury R, Ghossoub E. Diagnostic biomarkers of Alzheimer\u2019s disease: a state-of-the-art review. Biomark Neuropsychiatry. 2019;1:100005.","journal-title":"Biomark Neuropsychiatry"},{"issue":"1","key":"1047_CR9","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/S1474-4422(09)70299-6","volume":"9","author":"CR Jack","year":"2010","unstructured":"Jack CR, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, et al. Hypothetical model of dynamic biomarkers of the Alzheimer\u2019s pathological cascade. Lancet Neurol. 2010;9(1):119\u201328.","journal-title":"Lancet Neurol"},{"key":"1047_CR10","first-page":"519","volume":"10","author":"E Pellegrini","year":"2018","unstructured":"Pellegrini E, Ballerini L, Hernandez M d CV, Chappell FM, Gonz\u00e1lez-Castro V, Anblagan D, et al. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review. Alzheimers Dement Diagn Assess Dis Monit. 2018;10:519\u201335.","journal-title":"Alzheimers Dement Diagn Assess Dis Monit"},{"issue":"1s","key":"1047_CR11","first-page":"1","volume":"16","author":"M Tanveer","year":"2020","unstructured":"Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, et al. Machine learning techniques for the diagnosis of Alzheimer\u2019s disease: a review. ACM Trans Multimed Comput Commun Appl. 2020;16(1s):1\u201335.","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"issue":"15","key":"1047_CR12","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1212\/WNL.0b013e31826e26b7","volume":"79","author":"TD Koepsell","year":"2012","unstructured":"Koepsell TD, Monsell SE. Reversion from mild cognitive impairment to normal or near-normal cognition: risk factors and prognosis. Neurology. 2012;79(15):1591\u20138.","journal-title":"Neurology."},{"issue":"5","key":"1047_CR13","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1097\/JGP.0b013e3181629971","volume":"16","author":"NA Ranginwala","year":"2008","unstructured":"Ranginwala NA, Hynan LS, Weiner MF, White CL. Clinical criteria for the diagnosis of Alzheimer disease: still good after all these years. Am J Geriatr Psychiatry. 2008;16(5):384\u20138.","journal-title":"Am J Geriatr Psychiatry"},{"key":"1047_CR14","doi-asserted-by":"crossref","unstructured":"Ansart M, Epelbaum S, Bassignana G, B\u00f4ne A, Bottani S, Cattai T, et al. Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Medical Image Analysis. 2021;67:101848.","DOI":"10.1016\/j.media.2020.101848"},{"key":"1047_CR15","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.neuroimage.2014.11.049","volume":"107","author":"KK Leung","year":"2015","unstructured":"Leung KK, Malone IM, Ourselin S, Gunter JL, Bernstein MA, Thompson PM, et al. Effects of changing from non-accelerated to accelerated MRI for follow-up in brain atrophy measurement. NeuroImage. 2015;107:46\u201353.","journal-title":"NeuroImage."},{"key":"1047_CR16","first-page":"981","volume":"14","author":"C Lin","year":"2006","unstructured":"Lin C, Watson RE, Ward HA, Rydberg CH, Witte RJ, Bernstein MA. MP-RAGE compared to 3D IR SPGR for optimal T1 contrast and image quality in the brain at 3T. Proc Intl Soc Mag Reson Med. 2006;14:981.","journal-title":"Proc Intl Soc Mag Reson Med"},{"issue":"7623","key":"1047_CR17","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/538020a","volume":"538","author":"D Castelvecchi","year":"2016","unstructured":"Castelvecchi D. Can we open the black box of AI? Nature. 2016;538(7623):20\u20133.","journal-title":"Nature."},{"key":"1047_CR18","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.patcog.2016.10.009","volume":"63","author":"T Tong","year":"2017","unstructured":"Tong T, Gray K, Gao Q, Chen L, Rueckert D. Multi-modal classification of Alzheimer\u2019s disease using nonlinear graph fusion. Pattern Recognit. 2017;63:171\u201381.","journal-title":"Pattern Recognit"},{"issue":"8","key":"1047_CR19","doi-asserted-by":"publisher","first-page":"2475","DOI":"10.1007\/s00429-020-02136-0","volume":"225","author":"PR Raamana","year":"2020","unstructured":"Raamana PR, Strother SC. for the Australian Imaging Biomarkers, Lifestyle flagship study of ageing, for The Alzheimer\u2019s Disease Neuroimaging Initiative. Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight. Brain Struct Funct. 2020;225(8):2475\u201393.","journal-title":"Brain Struct Funct"},{"key":"1047_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101850","volume":"67","author":"K Hett","year":"2021","unstructured":"Hett K, Ta V-T, Oguz I, Manj\u00f3n JV, Coup\u00e9 P. Multi-scale graph-based grading for Alzheimer\u2019s disease prediction. Med Image Anal. 2021;67:101850.","journal-title":"Med Image Anal"},{"key":"1047_CR21","doi-asserted-by":"publisher","first-page":"S91","DOI":"10.1016\/j.neurobiolaging.2014.05.040","volume":"36","author":"PR Raamana","year":"2015","unstructured":"Raamana PR, Weiner MW, Wang L, Beg MF. Thickness network features for prognostic applications in dementia. Neurobiol Aging. 2015;36:S91\u2013102.","journal-title":"Neurobiol Aging"},{"issue":"18","key":"1047_CR22","doi-asserted-by":"publisher","first-page":"4756","DOI":"10.1523\/JNEUROSCI.0141-08.2008","volume":"28","author":"Y He","year":"2008","unstructured":"He Y, Chen Z, Evans A. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer\u2019s disease. J Neurosci. 2008;28(18):4756\u201366.","journal-title":"J Neurosci"},{"issue":"8","key":"1047_CR23","doi-asserted-by":"publisher","first-page":"3476","DOI":"10.1093\/cercor\/bhw128","volume":"26","author":"JB Pereira","year":"2016","unstructured":"Pereira JB, Mijalkov M, Kakaei E, Mecocci P, Vellas B, Tsolaki M, et al. Disrupted network topology in patients with stable and progressive mild cognitive impairment and Alzheimer\u2019s disease. Cereb Cortex. 2016;26(8):3476\u201393.","journal-title":"Cereb Cortex"},{"key":"1047_CR24","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.neurobiolaging.2017.09.011","volume":"61","author":"BM Tijms","year":"2018","unstructured":"Tijms BM, ten Kate M, Gouw AA, Borta A, Verfaillie S, Teunissen CE, et al. Gray matter networks and clinical progression in subjects with predementia Alzheimer\u2019s disease. Neurobiol Aging. 2018;61:75\u201381.","journal-title":"Neurobiol Aging"},{"key":"1047_CR25","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-3-030-65621-8_14","volume-title":"Distributed computing and Internet technology","author":"S Katiyar","year":"2021","unstructured":"Katiyar S, Rani TS, Bhavani SD. Exploring Alzheimer\u2019s disease network using social network analysis. In: Goswami D, Hoang TA, editors. Distributed computing and Internet technology. Cham: Springer International Publishing; 2021. p. 223\u201337. [cited 2021 Feb 9]. (Lecture Notes in Computer Science; vol. 12582). Available from: http:\/\/link.springer.com\/10.1007\/978-3-030-65621-8_14."},{"key":"1047_CR26","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.nicl.2015.01.007","volume":"7","author":"DJ Phillips","year":"2015","unstructured":"Phillips DJ, McGlaughlin A, Ruth D, Jager LR, Soldan A. Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer\u2019s disease: the importance of graph creation methods. NeuroImage Clin. 2015;7:377\u201390.","journal-title":"NeuroImage Clin"},{"issue":"1","key":"1047_CR27","doi-asserted-by":"publisher","first-page":"11592","DOI":"10.1038\/s41598-018-29927-0","volume":"8","author":"G M\u00e5rtensson","year":"2018","unstructured":"M\u00e5rtensson G, Pereira JB, Mecocci P, Vellas B, Tsolaki M, K\u0142oszewska I, et al. Stability of graph theoretical measures in structural brain networks in Alzheimer\u2019s disease. Sci Rep. 2018;8(1):11592.","journal-title":"Sci Rep"},{"issue":"1","key":"1047_CR28","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1038\/s41398-020-0798-6","volume":"10","author":"C Scarpazza","year":"2020","unstructured":"Scarpazza C, Ha M, Baecker L, Garcia-Dias R, Pinaya WHL, Vieira S, et al. Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders. Transl Psychiatry. 2020;10(1):107.","journal-title":"Transl Psychiatry"},{"issue":"9","key":"1047_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0222916","volume":"14","author":"R Delgado","year":"2019","unstructured":"Delgado R, Tibau X-A. Why Cohen\u2019s Kappa should be avoided as performance measure in classification. Gu Q, editor. PLoS One. 2019;14(9):e0222916.","journal-title":"PLoS One"},{"key":"1047_CR30","doi-asserted-by":"crossref","unstructured":"Dinga R, Penninx BWJH, Veltman DJ, Schmaal L, Marquand AF. Beyond accuracy: measures for assessing machine learning models, pitfalls and guidelines. bioRxiv. 2019; [cited 2019 Dec 2]; Available from: http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/743138.","DOI":"10.1101\/743138"},{"issue":"2","key":"1047_CR31","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1002\/jmri.22466","volume":"33","author":"TF Heston","year":"2011","unstructured":"Heston TF. Standardizing predictive values in diagnostic imaging research. J Magn Reson Imaging. 2011;33(2):505.","journal-title":"J Magn Reson Imaging"},{"key":"1047_CR32","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.neubiorev.2014.05.010","volume":"45","author":"D Prata","year":"2014","unstructured":"Prata D, Mechelli A, Kapur S. Clinically meaningful biomarkers for psychosis: a systematic and quantitative review. Neurosci Biobehav Rev. 2014;45:134\u201341.","journal-title":"Neurosci Biobehav Rev"},{"key":"1047_CR33","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neubiorev.2013.12.004","volume":"39","author":"ANV Ruigrok","year":"2014","unstructured":"Ruigrok ANV, Salimi-Khorshidi G, Lai M-C, Baron-Cohen S, Lombardo MV, Tait RJ, et al. A meta-analysis of sex differences in human brain structure. Neurosci Biobehav Rev. 2014;39:34\u201350.","journal-title":"Neurosci Biobehav Rev"},{"issue":"3","key":"1047_CR34","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s11065-014-9268-3","volume":"24","author":"SN Lockhart","year":"2014","unstructured":"Lockhart SN, DeCarli C. Structural imaging measures of brain aging. Neuropsychol Rev. 2014;24(3):271\u201389.","journal-title":"Neuropsychol Rev"},{"key":"1047_CR35","doi-asserted-by":"crossref","unstructured":"LaMontagne PJ, Benzinger TLS, Morris JC, Keefe S, Hornbeck R, Xiong C, et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. Radiol Imaging. 2019; [cited 2020 Aug 17]. Available from: http:\/\/medrxiv.org\/lookup\/doi\/10.1101\/2019.12.13.19014902.","DOI":"10.1101\/2019.12.13.19014902"},{"issue":"4","key":"1047_CR36","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.1016\/j.neuroimage.2008.10.037","volume":"44","author":"J Wonderlick","year":"2009","unstructured":"Wonderlick J, Ziegler D, Hosseinivarnamkhasti P, Locascio J, Bakkour A, Vanderkouwe A, et al. Reliability of MRI-derived cortical and subcortical morphometric measures: effects of pulse sequence, voxel geometry, and parallel imaging. NeuroImage. 2009;44(4):1324\u201333.","journal-title":"NeuroImage."},{"key":"1047_CR37","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.neuroimage.2015.03.026","volume":"113","author":"P Vemuri","year":"2015","unstructured":"Vemuri P, Senjem ML, Gunter JL, Lundt ES, Tosakulwong N, Weigand SD, et al. Accelerated vs. unaccelerated serial MRI based TBM-SyN measurements for clinical trials in Alzheimer\u2019s disease. NeuroImage. 2015;113:61\u20139.","journal-title":"NeuroImage."},{"issue":"3","key":"1047_CR38","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","volume":"33","author":"B Fischl","year":"2002","unstructured":"Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation. Neuron. 2002;33(3):341\u201355.","journal-title":"Neuron."},{"key":"1047_CR39","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.arr.2016.01.002","volume":"30","author":"L Pini","year":"2016","unstructured":"Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, et al. Brain atrophy in Alzheimer\u2019s disease and aging. Ageing Res Rev. 2016;30:25\u201348.","journal-title":"Ageing Res Rev"},{"issue":"11","key":"1047_CR40","doi-asserted-by":"publisher","first-page":"2885","DOI":"10.1093\/brain\/awl256","volume":"129","author":"V Singh","year":"2006","unstructured":"Singh V, Chertkow H, Lerch JP, Evans AC, Dorr AE, Kabani NJ. Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer\u2019s disease. Brain. 2006;129(11):2885\u201393.","journal-title":"Brain."},{"issue":"2","key":"1047_CR41","doi-asserted-by":"publisher","first-page":"e31083","DOI":"10.1371\/journal.pone.0031083","volume":"7","author":"T Liu","year":"2012","unstructured":"Liu T, Lipnicki DM, Zhu W, Tao D, Zhang C, Cui Y, et al. Cortical gyrification and sulcal spans in early stage Alzheimer\u2019s disease. Ginsberg SD, editor. PLoS One. 2012;7(2):e31083.","journal-title":"PLoS One"},{"issue":"5","key":"1047_CR42","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1586\/ern.13.45","volume":"13","author":"M Radanovic","year":"2013","unstructured":"Radanovic M, Pereira FRS, Stella F, Aprahamian I, Ferreira LK, Forlenza OV, et al. White matter abnormalities associated with Alzheimer\u2019s disease and mild cognitive impairment: a critical review of MRI studies. Expert Rev Neurother. 2013;13(5):483\u201393.","journal-title":"Expert Rev Neurother"},{"key":"1047_CR43","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neuroimage.2015.04.042","volume":"115","author":"JE Iglesias","year":"2015","unstructured":"Iglesias JE, Augustinack JC, Nguyen K, Player CM, Player A, Wright M, et al. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. NeuroImage. 2015;115:117\u201337.","journal-title":"NeuroImage."},{"issue":"7","key":"1047_CR44","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1038\/nrneurol.2009.94","volume":"5","author":"WM van der Flier","year":"2009","unstructured":"van der Flier WM, Scheltens P. Hippocampal volume loss and Alzheimer disease progression. Nat Rev Neurol. 2009;5(7):361\u20132.","journal-title":"Nat Rev Neurol"},{"issue":"3","key":"1047_CR45","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage. 2010;52(3):1059\u201369.","journal-title":"NeuroImage."},{"issue":"1","key":"1047_CR46","first-page":"2171","volume":"13","author":"F-A Fortin","year":"2012","unstructured":"Fortin F-A, De Rainville F-M, Gardner M-AG, Parizeau M, Gagn\u00e9 C. DEAP: evolutionary algorithms made easy. J Mach Learn Res. 2012;13(1):2171\u20135.","journal-title":"J Mach Learn Res"},{"issue":"6947","key":"1047_CR47","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1136\/bmj.309.6947.102","volume":"309","author":"DG Altman","year":"1994","unstructured":"Altman DG, Bland JM. Statistics notes: diagnostic tests 2: predictive values. BMJ. 1994;309(6947):102.","journal-title":"BMJ."},{"issue":"8","key":"1047_CR48","doi-asserted-by":"publisher","first-page":"777","DOI":"10.2174\/1567205015666180119092427","volume":"15","author":"M Davis","year":"2018","unstructured":"Davis M, O\u2019connell T, Johnson S, Cline S, Merikle E, Martenyi F, et al. Estimating Alzheimer\u2019s disease progression rates from normal cognition through mild cognitive impairment and stages of dementia. Curr Alzheimer Res. 2018;15(8):777\u201388.","journal-title":"Curr Alzheimer Res"},{"key":"1047_CR49","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neuroimage.2013.10.067","volume":"87","author":"S Haufe","year":"2014","unstructured":"Haufe S, Meinecke F, G\u00f6rgen K, D\u00e4hne S, Haynes J-D, Blankertz B, et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage. 2014;87:96\u2013110.","journal-title":"NeuroImage."},{"issue":"1","key":"1047_CR50","doi-asserted-by":"publisher","first-page":"35","DOI":"10.5093\/ejpalc2018a5","volume":"10","author":"JF Salgado","year":"2018","unstructured":"Salgado JF. Transforming the area under the normal curve (AUC) into Cohen\u2019s d, Pearson\u2019s r pb, odds-ratio, and natural log odds-ratio: two conversion tables. Eur J Psychol Appl Leg Context. 2018;10(1):35\u201347.","journal-title":"Eur J Psychol Appl Leg Context"},{"key":"1047_CR51","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.neunet.2020.03.017","volume":"126","author":"H Shahamat","year":"2020","unstructured":"Shahamat H, Saniee AM. Brain MRI analysis using a deep learning based evolutionary approach. Neural Netw. 2020;126:218\u201334.","journal-title":"Neural Netw"},{"issue":"03","key":"1047_CR52","doi-asserted-by":"publisher","first-page":"1650050","DOI":"10.1142\/S0129065716500507","volume":"27","author":"L Khedher","year":"2017","unstructured":"Khedher L, Ill\u00e1n IA, G\u00f3rriz JM, Ram\u00edrez J, Brahim A, Meyer-Baese A. Independent component analysis-support vector machine-based computer-aided diagnosis system for Alzheimer\u2019s with visual support. Int J Neural Syst. 2017;27(03):1650050.","journal-title":"Int J Neural Syst"},{"issue":"6","key":"1047_CR53","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1093\/brain\/awaa137","volume":"143","author":"S Qiu","year":"2020","unstructured":"Qiu S, Joshi PS, Miller MI, Xue C, Zhou X, Karjadi C, et al. Development and validation of an interpretable deep learning framework for Alzheimer\u2019s disease classification. Brain. 2020;143(6):1920\u201333.","journal-title":"Brain."},{"key":"1047_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116113","volume":"202","author":"E Lee","year":"2019","unstructured":"Lee E, Choi J-S, Kim M, Suk H-I. Toward an interpretable Alzheimer\u2019s disease diagnostic model with regional abnormality representation via deep learning. NeuroImage. 2019;202:116113.","journal-title":"NeuroImage."},{"issue":"4","key":"1047_CR55","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/TPAMI.2018.2889096","volume":"42","author":"C Lian","year":"2020","unstructured":"Lian C, Liu M, Zhang J, Shen D. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer\u2019s disease diagnosis using structural MRI. IEEE Trans Pattern Anal Mach Intell. 2020;42(4):880\u201393.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3\u20134","key":"1047_CR56","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s12021-018-9370-4","volume":"16","author":"M Liu","year":"2018","unstructured":"Liu M, Cheng D, Wang K, Wang Y. the Alzheimer\u2019s Disease Neuroimaging Initiative. Multi-modality cascaded convolutional neural networks for Alzheimer\u2019s disease diagnosis. Neuroinformatics. 2018;16(3\u20134):295\u2013308.","journal-title":"Neuroinformatics."},{"key":"1047_CR57","volume-title":"Reproducible evaluation of classification methods in Alzheimer\u2019s disease: Framework and application to MRI and PET data","author":"J Samper-Gonz\u00e1lez","year":"2018","unstructured":"Samper-Gonz\u00e1lez J. Reproducible evaluation of classification methods in Alzheimer\u2019s disease: Framework and application to MRI and PET data, vol. 18; 2018."},{"key":"1047_CR58","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.media.2017.10.005","volume":"43","author":"M Liu","year":"2018","unstructured":"Liu M, Zhang J, Adeli E, Shen D. Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal. 2018;43:157\u201368.","journal-title":"Med Image Anal"},{"key":"1047_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101694","volume":"63","author":"J Wen","year":"2020","unstructured":"Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-Gonz\u00e1lez J, Routier A, Bottani S, et al. Convolutional neural networks for classification of Alzheimer\u2019s disease: overview and reproducible evaluation. Med Image Anal. 2020;63:101694.","journal-title":"Med Image Anal"},{"key":"1047_CR60","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jneumeth.2017.12.005","volume":"302","author":"J Ram\u00edrez","year":"2018","unstructured":"Ram\u00edrez J, G\u00f3rriz JM, Ortiz A, Mart\u00ednez-Murcia FJ, Segovia F, Salas-Gonzalez D, et al. Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares. J Neurosci Methods. 2018;302:47\u201357.","journal-title":"J Neurosci Methods"},{"key":"1047_CR61","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.jneumeth.2017.11.013","volume":"302","author":"PA Donnelly-Kehoe","year":"2018","unstructured":"Donnelly-Kehoe PA, Pascariello GO, G\u00f3mez JC. Looking for Alzheimer\u2019s disease morphometric signatures using machine learning techniques. J Neurosci Methods. 2018;302:24\u201334.","journal-title":"J Neurosci Methods"},{"key":"1047_CR62","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.jneumeth.2018.01.003","volume":"302","author":"L S\u00f8rensen","year":"2018","unstructured":"S\u00f8rensen L, Nielsen M. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination. J Neurosci Methods. 2018;302:66\u201374.","journal-title":"J Neurosci Methods"},{"key":"1047_CR63","doi-asserted-by":"crossref","unstructured":"Maruszak A, Thuret S. Why looking at the whole hippocampus is not enough\u2014a critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for Alzheimer\u2019s disease diagnosis. Front Cell Neurosci. 2014;8 [cited 2020 Sep 15]. Available from: http:\/\/journal.frontiersin.org\/article\/10.3389\/fncel.2014.00095\/abstract.","DOI":"10.3389\/fncel.2014.00095"},{"issue":"2","key":"1047_CR64","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1097\/NEN.0b013e3181cb5af4","volume":"69","author":"MA Ansari","year":"2010","unstructured":"Ansari MA, Scheff SW. Oxidative stress in the progression of Alzheimer disease in the frontal cortex. J Neuropathol Exp Neurol. 2010;69(2):155\u201367.","journal-title":"J Neuropathol Exp Neurol"},{"issue":"4","key":"1047_CR65","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1212\/WNL.51.4.993","volume":"51","author":"CR Jack","year":"1998","unstructured":"Jack CR, Petersen RC, Xu Y, O\u2019Brien PC, Smith GE, Ivnik RJ, et al. Rate of medial temporal lobe atrophy in typical aging and Alzheimer\u2019s disease. Neurology. 1998;51(4):993\u20139.","journal-title":"Neurology."},{"key":"1047_CR66","doi-asserted-by":"crossref","unstructured":"Mutlu J, Landeau B, Tomadesso C, de Flores R, M\u00e9zenge F, de La Sayette V, et al. Connectivity disruption, atrophy, and hypometabolism within posterior cingulate networks in Alzheimer\u2019s disease. Front Neurosci. 2016;10 [cited 2020 Oct 29]. Available from: http:\/\/journal.frontiersin.org\/article\/10.3389\/fnins.2016.00582\/full.","DOI":"10.3389\/fnins.2016.00582"}],"container-title":["Alzheimer's Research &amp; Therapy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13195-022-01047-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13195-022-01047-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13195-022-01047-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T11:11:46Z","timestamp":1659525106000},"score":1,"resource":{"primary":{"URL":"https:\/\/alzres.biomedcentral.com\/articles\/10.1186\/s13195-022-01047-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,3]]},"references-count":67,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["1047"],"URL":"https:\/\/doi.org\/10.1186\/s13195-022-01047-y","relation":{},"ISSN":["1758-9193"],"issn-type":[{"value":"1758-9193","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,3]]},"assertion":[{"value":"15 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the institutional review boards of all participating institutions, and written informed consent was obtained from all participants or their authorized representatives.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"107"}}