{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:16:24Z","timestamp":1790072184387,"version":"4.0.1"},"reference-count":82,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:00:00Z","timestamp":1718236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:00:00Z","timestamp":1718236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neurol Sci"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s10072-024-07649-8","type":"journal-article","created":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T20:02:09Z","timestamp":1718222529000},"page":"5117-5127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers \u2013 A narrative review of a growing field"],"prefix":"10.1007","volume":"45","author":[{"given":"Thorsten","family":"Rudroff","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oona","family":"Rainio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riku","family":"Kl\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,13]]},"reference":[{"issue":"1","key":"7649_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1111\/ene.13439","volume":"25","author":"CA Lane","year":"2018","unstructured":"Lane CA, Hardy J, Schott JM (2018) Alzheimer\u2019s disease. Eur J Neurol 25(1):59\u201370. https:\/\/doi.org\/10.1111\/ene.13439","journal-title":"Eur J Neurol"},{"issue":"1","key":"7649_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/S1474-4422(18)30403-4","volume":"18","author":"E Nichols","year":"2019","unstructured":"Nichols E, Szoeke CEI, Vollset SE et al (2019) Global, regional, and national burden of Alzheimer\u2019s disease and other dementias, 1990\u20132016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 18(1):88\u2013106. https:\/\/doi.org\/10.1016\/S1474-4422(18)30403-4","journal-title":"Lancet Neurol"},{"issue":"3","key":"7649_CR3","doi-asserted-by":"publisher","first-page":"617","DOI":"10.3233\/JAD-150692","volume":"49","author":"B Dubois","year":"2016","unstructured":"Dubois B, Padovani A, Scheltens P, Rossi A, Dell\u2019Agnello G (2016) Timely diagnosis for Alzheimer\u2019s disease: a literature review on benefits and challenges. J Alzheimers Dis 49(3):617\u2013631. https:\/\/doi.org\/10.3233\/JAD-150692","journal-title":"J Alzheimers Dis"},{"issue":"10284","key":"7649_CR4","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1016\/S0140-6736(20)32205-4","volume":"397","author":"P Scheltens","year":"2021","unstructured":"Scheltens P, De Strooper B, Kivipelto M et al (2021) Alzheimer\u2019s disease. Lancet 397(10284):1577\u20131590. https:\/\/doi.org\/10.1016\/S0140-6736(20)32205-4","journal-title":"Lancet"},{"issue":"4","key":"7649_CR5","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","volume":"14","author":"CR Jack Jr","year":"2018","unstructured":"Jack CR Jr, Bennett DA, Blennow K et al (2018) NIA-AA Research Framework: Toward a biological definition of Alzheimer\u2019s disease. Alzheimers Dement 14(4):535\u2013562. https:\/\/doi.org\/10.1016\/j.jalz.2018.02.018","journal-title":"Alzheimers Dement"},{"key":"7649_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, Iftikhar MA, Shacklett A, Davatzikos C (2017) A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer\u2019s disease and its prodromal stages. Neuroimage 155:530\u2013548. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.03.057","journal-title":"Neuroimage"},{"key":"7649_CR7","doi-asserted-by":"publisher","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","volume":"63","author":"J Wen","year":"2020","unstructured":"Wen J, Thibeau-Sutre E, Diaz-Melo M et al (2020) Convolutional neural networks for classification of Alzheimer\u2019s disease: Overview and reproducible evaluation. Med Image Anal 63:101694. https:\/\/doi.org\/10.1016\/j.media.2020.101694","journal-title":"Med Image Anal"},{"key":"7649_CR8","doi-asserted-by":"publisher","first-page":"220","DOI":"10.3389\/fnagi.2019.00220","volume":"11","author":"T Jo","year":"2019","unstructured":"Jo T, Nho K, Saykin AJ (2019) Deep learning in Alzheimer\u2019s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 11:220. https:\/\/doi.org\/10.3389\/fnagi.2019.00220","journal-title":"Front Aging Neurosci"},{"issue":"1s","key":"7649_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3344998","volume":"16","author":"M Tanveer","year":"2020","unstructured":"Tanveer M, Richhariya B, Khan RU et al (2020) Machine learning techniques for the diagnosis of Alzheimer\u2019s disease: A review. ACM Trans Multim Comput Commun Appl 16(1s):1\u201335. https:\/\/doi.org\/10.1145\/3344998","journal-title":"ACM Trans Multim Comput Commun Appl"},{"key":"7649_CR10","doi-asserted-by":"publisher","first-page":"101848","DOI":"10.1016\/j.media.2020.101848","volume":"67","author":"M Ansart","year":"2021","unstructured":"Ansart M, Epelbaum S, Bassignana G et al (2021) Predicting the progression of mild cognitive impairment using machine learning: a systematic, quantitative and critical review. Med Image Anal 67:101848. https:\/\/doi.org\/10.1016\/j.media.2020.101848","journal-title":"Med Image Anal"},{"key":"7649_CR11","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 et al (2016) Brain atrophy in Alzheimer\u2019s Disease and aging. Ageing Res Rev 30:25\u201348. https:\/\/doi.org\/10.1016\/j.arr.2016.01.002","journal-title":"Ageing Res Rev"},{"issue":"10","key":"7649_CR12","doi-asserted-by":"publisher","first-page":"4922","DOI":"10.1002\/hbm.23704","volume":"38","author":"R de Flores","year":"2017","unstructured":"de Flores R, Mutlu J, Bejanin A et al (2017) Intrinsic connectivity of hippocampal subfields in normal elderly and mild cognitive impairment patients. Hum Brain Mapp 38(10):4922\u20134932. https:\/\/doi.org\/10.1002\/hbm.23704","journal-title":"Hum Brain Mapp"},{"key":"7649_CR13","doi-asserted-by":"publisher","unstructured":"Dickerson BC, Wolk DA (2013) Alzheimer's Disease Neuroimaging Initiative. Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-\u03b2 and tau. Front Aging Neurosci. 5:55. https:\/\/doi.org\/10.3389\/fnagi.2013.00055","DOI":"10.3389\/fnagi.2013.00055"},{"key":"7649_CR14","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.nicl.2016.06.010","volume":"12","author":"C Pettigrew","year":"2016","unstructured":"Pettigrew C, Soldan A, Zhu Y et al (2016) Cortical thickness in relation to clinical symptom onset in preclinical AD. Neuroimage Clin 12:116\u2013122. https:\/\/doi.org\/10.1016\/j.nicl.2016.06.010","journal-title":"Neuroimage Clin"},{"key":"7649_CR15","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.dadm.2017.03.007","volume":"8","author":"A Badhwar","year":"2017","unstructured":"Badhwar A, Tam A, Dansereau C, Orban P, Hoffstaedter F, Bellec P (2017) Resting-state network dysfunction in Alzheimer\u2019s disease: A systematic review and meta-analysis. Alzheimers Dement (Amst) 8:73\u201385. https:\/\/doi.org\/10.1016\/j.dadm.2017.03.007","journal-title":"Alzheimers Dement (Amst)"},{"issue":"1","key":"7649_CR16","doi-asserted-by":"publisher","first-page":"107","DOI":"10.3233\/JAD-180847","volume":"70","author":"LT Eyler","year":"2019","unstructured":"Eyler LT, Elman JA, Hatton SN et al (2019) Resting state abnormalities of the default mode network in mild cognitive impairment: A systematic review and meta-analysis. J Alzheimers Dis 70(1):107\u2013120. https:\/\/doi.org\/10.3233\/JAD-180847","journal-title":"J Alzheimers Dis"},{"issue":"9","key":"7649_CR17","doi-asserted-by":"publisher","first-page":"3716","DOI":"10.1007\/s00330-017-4768-1","volume":"27","author":"JM Papma","year":"2017","unstructured":"Papma JM, Smits M, de Groot M et al (2017) The effect of hippocampal function, volume and connectivity on posterior cingulate cortex functioning during episodic memory fMRI in mild cognitive impairment. Eur Radiol 27(9):3716\u20133724. https:\/\/doi.org\/10.1007\/s00330-017-4768-1","journal-title":"Eur Radiol"},{"issue":"1","key":"7649_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4306\/pi.2016.13.1.1","volume":"13","author":"SH Joo","year":"2016","unstructured":"Joo SH, Lim HK, Lee CU (2016) Three large-scale functional brain networks from resting-state functional MRI in subjects with different levels of cognitive impairment. Psychiatry Investig 13(1):1\u20137. https:\/\/doi.org\/10.4306\/pi.2016.13.1.1","journal-title":"Psychiatry Investig"},{"issue":"1","key":"7649_CR19","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1136\/jnnp.74.1.44","volume":"74","author":"RA Sperling","year":"2003","unstructured":"Sperling RA, Bates JF, Chua EF, Cocchiarella AJ, Rentz DM, Rosen BR, Schacter DL, Albert MS (2003) fMRI studies of associative encoding in young and elderly controls and mild Alzheimer\u2019s disease. J Neurol Neurosurg Psychiatry 74(1):44\u201350","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"1","key":"7649_CR20","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.neuroimage.2004.10.045","volume":"25","author":"F Remy","year":"2005","unstructured":"Remy F, Mirrashed F, Campbell B, Richter W (2005) Verbal episodic memory impairment in Alzheimer\u2019s disease: a combined structural and functional MRI study. Neuroimage 25(1):253\u2013266. https:\/\/doi.org\/10.1016\/j.neuroimage.2004.10.045","journal-title":"Neuroimage"},{"issue":"1","key":"7649_CR21","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s00330-005-2794-x","volume":"16","author":"FZ Yetkin","year":"2006","unstructured":"Yetkin FZ, Rosenberg RN, Weiner MF, Purdy PD, Cullum CM (2006) FMRI of working memory in patients with mild cognitive impairment and probable Alzheimer\u2019s disease. Eur Radiol 16(1):193\u2013206. https:\/\/doi.org\/10.1007\/s00330-005-2794-x","journal-title":"Eur Radiol"},{"key":"7649_CR22","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ejrad.2017.07.014","volume":"94","author":"L Rice","year":"2017","unstructured":"Rice L, Bisdas S (2017) The diagnostic value of FDG and amyloid PET in Alzheimer\u2019s disease-A systematic review. Eur J Radiol 94:16\u201324. https:\/\/doi.org\/10.1016\/j.ejrad.2017.07.014","journal-title":"Eur J Radiol"},{"issue":"1","key":"7649_CR23","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s13195-019-0512-1","volume":"11","author":"YN Ou","year":"2019","unstructured":"Ou YN, Xu W, Li JQ et al (2019) FDG-PET as an independent biomarker for Alzheimer\u2019s biological diagnosis: a longitudinal study. Alzheimers Res Ther 11(1):57. https:\/\/doi.org\/10.1186\/s13195-019-0512-1","journal-title":"Alzheimers Res Ther"},{"issue":"1","key":"7649_CR24","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1038\/s41467-017-01150-x","volume":"8","author":"S Palmqvist","year":"2017","unstructured":"Palmqvist S, Sch\u00f6ll M, Strandberg O et al (2017) Earliest accumulation of \u03b2-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat Commun 8(1):1214. https:\/\/doi.org\/10.1038\/s41467-017-01150-x","journal-title":"Nat Commun"},{"issue":"8","key":"7649_CR25","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1001\/jamaneurol.2019.1424","volume":"76","author":"BJ Hanseeuw","year":"2019","unstructured":"Hanseeuw BJ, Betensky RA, Jacobs HIL et al (2019) Association of Amyloid and Tau With Cognition in Preclinical Alzheimer Disease: A Longitudinal Study. JAMA Neurol 76(8):915\u2013924. https:\/\/doi.org\/10.1001\/jamaneurol.2019.1424","journal-title":"JAMA Neurol"},{"issue":"Pt 5","key":"7649_CR26","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1093\/brain\/aww027","volume":"139","author":"R Ossenkoppele","year":"2016","unstructured":"Ossenkoppele R, Schonhaut DR, Sch\u00f6ll M et al (2016) Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer\u2019s disease. Brain 139(Pt 5):1551\u20131567. https:\/\/doi.org\/10.1093\/brain\/aww027","journal-title":"Brain"},{"issue":"6","key":"7649_CR27","doi-asserted-by":"publisher","first-page":"1723","DOI":"10.1093\/brain\/awz090","volume":"142","author":"MJ Pontecorvo","year":"2019","unstructured":"Pontecorvo MJ, Devous MD Sr, Kennedy I et al (2019) A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer\u2019s disease dementia. Brain 142(6):1723\u20131735. https:\/\/doi.org\/10.1093\/brain\/awz090","journal-title":"Brain"},{"issue":"2","key":"7649_CR28","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1007\/s00259-015-3228-x","volume":"43","author":"E Morris","year":"2016","unstructured":"Morris E, Chalkidou A, Hammers A, Peacock J, Summers J, Keevil S (2016) Diagnostic accuracy of (18)F amyloid PET tracers for the diagnosis of Alzheimer\u2019s disease: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 43(2):374\u2013385. https:\/\/doi.org\/10.1007\/s00259-015-3228-x","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"8","key":"7649_CR29","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1038\/s41380-018-0342-8","volume":"24","author":"A Leuzy","year":"2019","unstructured":"Leuzy A, Chiotis K, Lemoine L et al (2019) Tau PET imaging in neurodegenerative tauopathies-still a challenge. Mol Psychiatry 24(8):1112\u20131134. https:\/\/doi.org\/10.1038\/s41380-018-0342-8","journal-title":"Mol Psychiatry"},{"key":"7649_CR30","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.neuroimage.2018.08.042","volume":"183","author":"J Samper-Gonz\u00e1lez","year":"2018","unstructured":"Samper-Gonz\u00e1lez J, Burgos N, Bottani S et al (2018) Reproducible evaluation of classification methods in Alzheimer\u2019s disease: Framework and application to MRI and PET data. Neuroimage 183:504\u2013521. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.08.042","journal-title":"Neuroimage"},{"issue":"5","key":"7649_CR31","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.jalz.2013.09.003","volume":"10","author":"J Nettiksimmons","year":"2014","unstructured":"Nettiksimmons J, DeCarli C, Landau S, Beckett L (2014) Biological heterogeneity in ADNI amnestic mild cognitive impairment. Alzheimers Dement 10(5):511-521.e1. https:\/\/doi.org\/10.1016\/j.jalz.2013.09.003","journal-title":"Alzheimers Dement"},{"issue":"1","key":"7649_CR32","doi-asserted-by":"publisher","first-page":"6763","DOI":"10.1038\/s41598-017-06624-y","volume":"7","author":"D Gamberger","year":"2017","unstructured":"Gamberger D, Lavra\u010d N, Srivatsa S, Tanzi RE, Doraiswamy PM (2017) Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer\u2019s disease. Sci Rep 7(1):6763. https:\/\/doi.org\/10.1038\/s41598-017-06624-y","journal-title":"Sci Rep"},{"issue":"4","key":"7649_CR33","doi-asserted-by":"publisher","DOI":"10.1002\/wid","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi O, Rokach L (2018) Ensemble learning: A survey. WIREs Data Min Knowl Discovery 8(4):e1249. https:\/\/doi.org\/10.1002\/wid","journal-title":"WIREs Data Min Knowl Discovery"},{"key":"7649_CR34","doi-asserted-by":"publisher","first-page":"101645","DOI":"10.1016\/j.nicl.2018.101645","volume":"21","author":"S Basaia","year":"2019","unstructured":"Basaia S, Agosta F, Wagner L et al (2019) Automated classification of Alzheimer\u2019s disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin 21:101645. https:\/\/doi.org\/10.1016\/j.nicl.2018.101645","journal-title":"Neuroimage Clin"},{"issue":"2","key":"7649_CR35","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1148\/radiol.2018180958","volume":"290","author":"Y Ding","year":"2019","unstructured":"Ding Y, Sohn JH, Kawczynski MG et al (2019) A deep learning model to predict a diagnosis of Alzheimer Disease by using 18F-FDG PET of the brain. Radiology 290(2):456\u2013464. https:\/\/doi.org\/10.1148\/radiol.2018180958","journal-title":"Radiology"},{"key":"7649_CR36","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision 618--626. https:\/\/doi.org\/10.1109\/ICCV.2017.74.","DOI":"10.1109\/ICCV.2017.74"},{"key":"7649_CR37","doi-asserted-by":"publisher","unstructured":"Ji J (2019) Gradient-based interpretation on convolutional neural network for classification of pathological images. In: Proceeding of the International Conference on Information Technology and Computer Application, ITCA 83--86. https:\/\/doi.org\/10.1109\/ITCA49981.2019.00026","DOI":"10.1109\/ITCA49981.2019.00026"},{"issue":"6","key":"7649_CR38","doi-asserted-by":"publisher","first-page":"318","DOI":"10.3390\/INFO11060318","volume":"11","author":"K Kowsari","year":"2020","unstructured":"Kowsari K, Sali R, Ehsan L et al (2020) HMIC: hierarchical medical image classification, a deep learning approach. Information 11(6):318. https:\/\/doi.org\/10.3390\/INFO11060318","journal-title":"Information"},{"key":"7649_CR39","doi-asserted-by":"publisher","unstructured":"Windisch P, Weber P, F\u00fcrweger C, et al. Implementation of model explainability for a basic brain tumor detection using convolutional neural networks on MRI slices. Neuroradiology. Published online June 5, 2020:1\u201311. https:\/\/doi.org\/10.1007\/s00234-020-02465-1","DOI":"10.1007\/s00234-020-02465-1"},{"key":"7649_CR40","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3389\/fnagi.2019.00194","volume":"10","author":"M B\u00f6hle","year":"2019","unstructured":"B\u00f6hle M, Eitel F, Weygandt M, Ritter K (2019) Layer-wise relevance propagation for explaining deep neural network decisions in MRI-based Alzheimer\u2019s disease classification. Front Aging Neurosci 10:194. https:\/\/doi.org\/10.3389\/fnagi.2019.00194","journal-title":"Front Aging Neurosci"},{"key":"7649_CR41","unstructured":"Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M. Striving for simplicity: the all convolutional net. arXiv:14126806 [cs]. Published online December 21, 2014. Accessed March 16, 2024. http:\/\/arxiv.org\/abs\/1412.6806"},{"key":"7649_CR42","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.media.2019.01.004","volume":"53","author":"MM Ghazi","year":"2019","unstructured":"Ghazi MM, Nielsen M, Pai A et al (2019) Training recurrent neural networks robust to incomplete data: application to Alzheimer\u2019s disease progression modeling. Med Image Anal 53:39\u201346. https:\/\/doi.org\/10.1016\/j.media.2019.01.004","journal-title":"Med Image Anal"},{"key":"7649_CR43","doi-asserted-by":"publisher","unstructured":"Bhagwat N, Viviano JD, Voineskos AN, Chakravarty MM; Alzheimer's Disease Neuroimaging Initiative. Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data. PLoS Comput Biol. 2018;14(9):e1006376. https:\/\/doi.org\/10.1371\/journal.pcbi.1006376","DOI":"10.1371\/journal.pcbi.1006376"},{"key":"7649_CR44","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","volume":"101","author":"HI Suk","year":"2014","unstructured":"Suk HI, Lee SW, Shen D (2014) Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis. Neuroimage 101:569\u2013582. https:\/\/doi.org\/10.1016\/j.neuroimage.2014.06.077","journal-title":"Neuroimage"},{"issue":"1","key":"7649_CR45","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/TCBB.2017.2776910","volume":"16","author":"R Ju","year":"2019","unstructured":"Ju R, Hu C, Zhou P, Li Q (2019) Early diagnosis of Alzheimer\u2019s disease based on resting-state brain networks and deep learning. IEEE\/ACM Trans Comput Biol Bioinform 16(1):244\u2013257. https:\/\/doi.org\/10.1109\/TCBB.2017.2776910","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"7649_CR46","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 (2018) Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal 43:157\u2013168. https:\/\/doi.org\/10.1016\/j.media.2017.10.005","journal-title":"Med Image Anal"},{"issue":"1","key":"7649_CR47","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/JBHI.2017.2655720","volume":"22","author":"J Shi","year":"2018","unstructured":"Shi J, Zheng X, Li Y, Zhang Q, Ying S (2018) Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer\u2019s Disease. IEEE J Biomed Health Inform 22(1):173\u2013183. https:\/\/doi.org\/10.1109\/JBHI.2017.2655720","journal-title":"IEEE J Biomed Health Inform"},{"key":"7649_CR48","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3389\/fnins.2014.00229","volume":"8","author":"SM Plis","year":"2014","unstructured":"Plis SM, Hjelm DR, Salakhutdinov R et al (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229. https:\/\/doi.org\/10.3389\/fnins.2014.00229","journal-title":"Front Neurosci"},{"issue":"5","key":"7649_CR49","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10916-018-0932-7","volume":"42","author":"SH Wang","year":"2018","unstructured":"Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H (2018) Classification of Alzheimer\u2019s Disease Based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42(5):85. https:\/\/doi.org\/10.1007\/s10916-018-0932-7","journal-title":"J Med Syst"},{"key":"7649_CR50","doi-asserted-by":"publisher","first-page":"259","DOI":"10.3389\/fnins.2020.00259","volume":"14","author":"D Pan","year":"2020","unstructured":"Pan D, Zeng A, Jia L, Huang Y, Frizzell T, Song X (2020) Early detection of Alzheimer\u2019s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front Neurosci 14:259. https:\/\/doi.org\/10.3389\/fnins.2020.00259","journal-title":"Front Neurosci"},{"issue":"3","key":"7649_CR51","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1038\/nn.4478","volume":"20","author":"CW Woo","year":"2017","unstructured":"Woo CW, Chang LJ, Lindquist MA, Wager TD (2017) Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci 20(3):365\u2013377. https:\/\/doi.org\/10.1038\/nn.4478","journal-title":"Nat Neurosci"},{"issue":"3","key":"7649_CR52","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.bpsc.2015.12.005","volume":"1","author":"VD Calhoun","year":"2016","unstructured":"Calhoun VD, Sui J (2016) Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness. Biol Psych Cognit Neurosci Neuroimag 1(3):230\u2013244. https:\/\/doi.org\/10.1016\/j.bpsc.2015.12.005","journal-title":"Biol Psych Cognit Neurosci Neuroimag"},{"key":"7649_CR53","doi-asserted-by":"publisher","unstructured":"Bouts MJRJ, van der Grond J, Vernooij MW, Koini M, Schouten TM, de Vos F ... Rombouts SARB (2021) Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. Human Brain Mapping, 42(9), 2819\u20132831. https:\/\/doi.org\/10.1002\/hbm.25388","DOI":"10.1002\/hbm.25388"},{"key":"7649_CR54","doi-asserted-by":"publisher","unstructured":"Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R ... Feng D (2015) Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Trans Biomed Eng, 62(4), 1132\u20131140. https:\/\/doi.org\/10.1109\/TBME.2014.2372011","DOI":"10.1109\/TBME.2014.2372011"},{"issue":"3","key":"7649_CR55","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","volume":"55","author":"D Zhang","year":"2011","unstructured":"Zhang D, Wang Y, Zhou L, Yuan H, Shen D (2011) Multimodal classification of Alzheimer\u2019s disease and mild cognitive impairment. Neuroimage 55(3):856\u2013867. https:\/\/doi.org\/10.1016\/j.neuroimage.2011.01.008","journal-title":"Neuroimage"},{"issue":"7","key":"7649_CR56","doi-asserted-by":"publisher","first-page":"P293","DOI":"10.1016\/j.jalz.2018.06.070","volume":"14","author":"S Paquerault","year":"2018","unstructured":"Paquerault S, Allard M, Grigis A et al (2018) Combining multiple imaging and non-imaging biomarkers to improve early prediction of Alzheimer\u2019s disease. Alzheimers Dement 14(7):P293\u2013P294. https:\/\/doi.org\/10.1016\/j.jalz.2018.06.070","journal-title":"Alzheimers Dement"},{"key":"7649_CR57","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.nicl.2015.05.006","volume":"8","author":"SJ Teipel","year":"2015","unstructured":"Teipel SJ, Kurth J, Krause B, Grothe MJ (2015) The relative importance of imaging markers for the prediction of Alzheimer\u2019s disease dementia in mild cognitive impairment - Beyond classical regression. Neuroimage Clin 8:583\u2013593. https:\/\/doi.org\/10.1016\/j.nicl.2015.05.006","journal-title":"Neuroimage Clin"},{"key":"7649_CR58","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3389\/fnagi.2017.00006","volume":"9","author":"B Lei","year":"2017","unstructured":"Lei B, Jiang F, Chen S, Ni D, Wang T (2017) Longitudinal analysis for disease progression via simultaneous multi-relational temporal-fused learning. Front Aging Neurosci 9:6. https:\/\/doi.org\/10.3389\/fnagi.2017.00006","journal-title":"Front Aging Neurosci"},{"issue":"4","key":"7649_CR59","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.3233\/JAD-160927","volume":"56","author":"A Singanamalli","year":"2017","unstructured":"Singanamalli A, Wang H, Madabhushi A, Initiative ADN (2017) A supervised graph-based approach for the early diagnosis of Alzheimer\u2019s disease using resting-state fMRI data. J Alzheimers Dis 56(4):1263\u20131280. https:\/\/doi.org\/10.3233\/JAD-160927","journal-title":"J Alzheimers Dis"},{"key":"7649_CR60","doi-asserted-by":"publisher","unstructured":"Marinescu RV, Oxtoby NP, Young AL, et al. (2018) TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease. arXiv [preprint]. arXiv:1805.03909. https:\/\/doi.org\/10.48550\/arXiv.1805.03909","DOI":"10.48550\/arXiv.1805.03909"},{"key":"7649_CR61","unstructured":"Enders CK (2010) Applied missing data analysis. Guilford press"},{"key":"7649_CR62","doi-asserted-by":"publisher","first-page":"91","DOI":"10.2147\/CLEP.S72247","volume":"7","author":"K Biering","year":"2015","unstructured":"Biering K, Hjollund NH, Frydenberg M (2015) Using multiple imputation to deal with missing data and attrition in longitudinal studies with repeated measures of patient-reported outcomes. Clin Epidemiol 7:91\u2013106. https:\/\/doi.org\/10.2147\/CLEP.S72247","journal-title":"Clin Epidemiol"},{"issue":"1","key":"7649_CR63","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s12874-017-0442-1","volume":"17","author":"JC Jakobsen","year":"2017","unstructured":"Jakobsen JC, Gluud C, Wetterslev J, Winkel P (2017) When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol 17(1):162. https:\/\/doi.org\/10.1186\/s12874-017-0442-1","journal-title":"BMC Med Res Methodol"},{"issue":"524","key":"7649_CR64","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.1080\/01621459.2017.1381740","volume":"113","author":"W Miao","year":"2018","unstructured":"Miao W, Tchetgen Tchetgen EJ, Geng Z (2018) Identification and Doubly Robust Estimation of Data Missing Not at Random With an Ancillary Variable. J Am Stat Assoc 113(524):1718\u20131734. https:\/\/doi.org\/10.1080\/01621459.2017.1381740","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"7649_CR65","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s10115-015-0822-y","volume":"46","author":"MG Rahman","year":"2016","unstructured":"Rahman MG, Islam MZ (2016) Missing value imputation using a fuzzy clustering-based EM approach. Knowl Inf Syst 46(2):389\u2013422. https:\/\/doi.org\/10.1007\/s10115-015-0822-y","journal-title":"Knowl Inf Syst"},{"issue":"6","key":"7649_CR66","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/sam.11348","volume":"10","author":"F Tang","year":"2017","unstructured":"Tang F, Ishwaran H (2017) Random forest missing data algorithms. Stat Anal Data Mining: The ASA Data Sci J 10(6):363\u2013377. https:\/\/doi.org\/10.1002\/sam.11348","journal-title":"Stat Anal Data Mining: The ASA Data Sci J"},{"issue":"3","key":"7649_CR67","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","volume":"74","author":"RC Petersen","year":"2010","unstructured":"Petersen RC, Aisen PS, Beckett LA et al (2010) Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 74(3):201\u2013209. https:\/\/doi.org\/10.1212\/WNL.0b013e3181cb3e25","journal-title":"Neurology"},{"issue":"4","key":"7649_CR68","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1017\/S1041610209009405","volume":"21","author":"KA Ellis","year":"2009","unstructured":"Ellis KA, Bush AI, Darby D et al (2009) The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer\u2019s disease. Int Psychogeriatr 21(4):672\u2013687. https:\/\/doi.org\/10.1017\/S1041610209009405","journal-title":"Int Psychogeriatr"},{"key":"7649_CR69","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.neuroimage.2017.02.035","volume":"170","author":"C Wachinger","year":"2018","unstructured":"Wachinger C, Reuter M, Klein T (2018) DeepNAT: Deep convolutional neural network for segmenting neuroanatomy. Neuroimage 170:434\u2013445. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.02.035","journal-title":"Neuroimage"},{"issue":"1","key":"7649_CR70","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s13024-017-0162-3","volume":"12","author":"L Saint-Aubert","year":"2017","unstructured":"Saint-Aubert L, Lemoine L, Chiotis K, Leuzy A, Rodriguez-Vieitez E, Nordberg A (2017) Tau PET imaging: present and future directions. Mol Neurodegener 12(1):19. https:\/\/doi.org\/10.1186\/s13024-017-0162-3","journal-title":"Mol Neurodegener"},{"issue":"4","key":"7649_CR71","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CR Jack Jr","year":"2008","unstructured":"Jack CR Jr, Bernstein MA, Fox NC et al (2008) The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging 27(4):685\u2013691. https:\/\/doi.org\/10.1002\/jmri.21049","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"7649_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke N, Hancox J, Li W et al (2020) The future of digital health with federated learning. NPJ Digit Med 3(1):1\u20137. https:\/\/doi.org\/10.1038\/s41746-020-00323-1","journal-title":"NPJ Digit Med"},{"key":"7649_CR73","doi-asserted-by":"publisher","unstructured":"Arslan S, Ktena SI, Glocker B, Rueckert D. Graph saliency maps through spectral convolutional networks: Application to sex classification with brain connectivity. In: Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities. Springer, Cham; 2018:3\u201313. https:\/\/doi.org\/10.1007\/978-3-030-00689-1_1","DOI":"10.1007\/978-3-030-00689-1_1"},{"key":"7649_CR74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2018","unstructured":"Montavon G, Samek W, M\u00fcller KR (2018) Methods for interpreting and understanding deep neural networks. Digit Signal Process 73:1\u201315. https:\/\/doi.org\/10.1016\/j.dsp.2017.10.011","journal-title":"Digit Signal Process"},{"key":"7649_CR75","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1016\/j.neuroimage.2018.10.003","volume":"197","author":"C Davatzikos","year":"2019","unstructured":"Davatzikos C (2019) Machine learning in neuroimaging: Progress and challenges. Neuroimage 197:652\u2013656. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.10.003","journal-title":"Neuroimage"},{"issue":"10","key":"7649_CR76","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1001\/jamaneurol.2017.2188","volume":"74","author":"SC Neu","year":"2017","unstructured":"Neu SC, Pa J, Kukull W et al (2017) Apolipoprotein E genotype and sex risk factors for Alzheimer disease: a meta-analysis. JAMA Neurol 74(10):1178\u20131189. https:\/\/doi.org\/10.1001\/jamaneurol.2017.2188","journal-title":"JAMA Neurol"},{"issue":"1","key":"7649_CR77","doi-asserted-by":"publisher","first-page":"4273","DOI":"10.1038\/s41467-018-05892-0","volume":"9","author":"AL Young","year":"2018","unstructured":"Young AL, Marinescu RV, Oxtoby NP et al (2018) Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun 9(1):4273. https:\/\/doi.org\/10.1038\/s41467-018-05892-0","journal-title":"Nat Commun"},{"issue":"1","key":"7649_CR78","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.bushor.2018.08.004","volume":"62","author":"A Kaplan","year":"2019","unstructured":"Kaplan A, Haenlein M (2019) Siri, Siri, in my hand: Who\u2019s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus Horiz 62(1):15\u201325. https:\/\/doi.org\/10.1016\/j.bushor.2018.08.004","journal-title":"Bus Horiz"},{"issue":"1","key":"7649_CR79","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44\u201356. https:\/\/doi.org\/10.1038\/s41591-018-0300-7","journal-title":"Nat Med"},{"key":"7649_CR80","unstructured":"U.S. Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence\/machine learning (AI\/ML)-based software as a medical device (SaMD). White Paper. 2019. https:\/\/www.fda.gov\/media\/122535\/download"},{"issue":"2","key":"7649_CR81","doi-asserted-by":"publisher","first-page":"205395171667967","DOI":"10.1177\/2053951716679679","volume":"3","author":"BD Mittelstadt","year":"2016","unstructured":"Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L (2016) The ethics of algorithms: Mapping the debate. Big Data Soc 3(2):2053951716679679. https:\/\/doi.org\/10.1177\/2053951716679679","journal-title":"Big Data Soc"},{"issue":"11","key":"7649_CR82","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1056\/NEJMp1714229","volume":"378","author":"DS Char","year":"2018","unstructured":"Char DS, Shah NH, Magnus D (2018) Implementing machine learning in health care\u2014addressing ethical challenges. N Engl J Med 378(11):981\u2013983. https:\/\/doi.org\/10.1056\/NEJMp1714229","journal-title":"N Engl J Med"}],"container-title":["Neurological Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10072-024-07649-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10072-024-07649-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10072-024-07649-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T00:05:03Z","timestamp":1728691503000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10072-024-07649-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,13]]},"references-count":82,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["7649"],"URL":"https:\/\/doi.org\/10.1007\/s10072-024-07649-8","relation":{},"ISSN":["1590-1874","1590-3478"],"issn-type":[{"value":"1590-1874","type":"print"},{"value":"1590-3478","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,13]]},"assertion":[{"value":"24 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable since no participants were recruited during the execution of this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}},{"value":"This manuscript has been approved for publication by all authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}