{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:48:53Z","timestamp":1782989333600,"version":"3.54.5"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Dubai Future Foundation","award":["2024CANAD-KAM-060"],"award-info":[{"award-number":["2024CANAD-KAM-060"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1186\/s40708-026-00310-4","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T06:52:59Z","timestamp":1781851979000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RDoC-informed explainable AI as a paradigm for multilevel Alzheimer\u2019s disease diagnosis and progression prediction: a systematic review"],"prefix":"10.1186","volume":"13","author":[{"given":"Mohammad","family":"Nami","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Peebles","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fadi","family":"Thabtah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firuz","family":"Kamalov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,19]]},"reference":[{"key":"310_CR1","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1093\/procel\/pwae026","volume":"16","author":"Q Zheng","year":"2025","unstructured":"Zheng Q, Wang X (2025) Alzheimer\u2019s disease: insights into pathology, molecular mechanisms, and therapy. Protein Cell 16:83\u2013120","journal-title":"Protein Cell"},{"key":"310_CR2","doi-asserted-by":"publisher","first-page":"4094","DOI":"10.1093\/brain\/awae211","volume":"147","author":"DE Peretti","year":"2024","unstructured":"Peretti DE, Boccalini C, Ribaldi F, Scheffler M, Marizzoni M, Ashton NJ, Zetterberg H, Blennow K, Frisoni GB, Garibotto V (2024) Association of glial fibrillary acid protein, Alzheimer\u2019s disease pathology and cognitive decline. Brain 147:4094\u20134104","journal-title":"Brain"},{"key":"310_CR3","doi-asserted-by":"publisher","first-page":"81","DOI":"10.31887\/DCNS.2020.22.1\/bcuthbert","volume":"22","author":"BN Cuthbert","year":"2020","unstructured":"Cuthbert BN (2020) The role of RDoC in future classification of mental disorders. Dialog Clin Neurosci 22:81\u201385","journal-title":"Dialog Clin Neurosci"},{"key":"310_CR4","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1176\/appi.ajp.2010.09091379","volume":"167","author":"T Insel","year":"2010","unstructured":"Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, Wang P (2010) Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry 167:748\u2013751","journal-title":"Am J Psychiatry"},{"key":"310_CR5","doi-asserted-by":"publisher","first-page":"11338","DOI":"10.3390\/ijms252111338","volume":"25","author":"KJ Kwon","year":"2024","unstructured":"Kwon KJ, Kim HY, Han S-H, Shin CY (2024) Future therapeutic strategies for Alzheimer\u2019s disease: focus on behavioral and psychological symptoms. Int J Mol Sci 25:11338","journal-title":"Int J Mol Sci"},{"key":"310_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1002\/hast.973","volume":"49","author":"AJ London","year":"2019","unstructured":"London AJ (2019) Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep 49:15\u201321","journal-title":"Hastings Cent Rep"},{"key":"310_CR7","doi-asserted-by":"publisher","first-page":"102158","DOI":"10.1016\/j.artmed.2021.102158","volume":"124","author":"TP Quinn","year":"2022","unstructured":"Quinn TP, Jacobs S, Senadeera M, Le V, Coghlan S (2022) The three ghosts of medical AI: Can the black-box present deliver? Artif Intell Med 124:102158","journal-title":"Artif Intell Med"},{"key":"310_CR8","doi-asserted-by":"publisher","first-page":"2620","DOI":"10.1007\/s00330-024-11158-9","volume":"35","author":"J Jiang","year":"2025","unstructured":"Jiang J, Li C, Lu J, Sun J, Sun X, Yang J, Wang L, Zuo C, Shi K, Initiative A (2025) s.D.N. Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [18F] FDG PET study. Eur Radiol 35:2620\u20132633","journal-title":"Eur Radiol"},{"key":"310_CR9","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1038\/s43587-023-00410-4","volume":"3","author":"MS Rafii","year":"2023","unstructured":"Rafii MS, Aisen PS (2023) Detection and treatment of Alzheimer\u2019s disease in its preclinical stage. Nat aging 3:520\u2013531","journal-title":"Nat aging"},{"key":"310_CR10","doi-asserted-by":"publisher","first-page":"16376","DOI":"10.1109\/ACCESS.2023.3244952","volume":"11","author":"FJM Shamrat","year":"2023","unstructured":"Shamrat FJM, Akter S, Azam S, Karim A, Ghosh P, Tasnim Z, Hasib KM, De Boer F, Ahmed K (2023) AlzheimerNet: An effective deep learning based proposition for alzheimer\u2019s disease stages classification from functional brain changes in magnetic resonance images. IEEE Access 11:16376\u201316395","journal-title":"IEEE Access"},{"key":"310_CR11","unstructured":"Li J-Q, Song J-H, Suckling J, Wang Y-J, Zuo C-T, Zhang C, Song Y-Q, Xie A-M, Tan L, Yu J-T (2023) Disease trajectories in elders with suspected non-Alzheimer\u2019s pathophysiology and its comparison with Alzheimer\u2019s disease pathophysiology: a longitudinal study. Res Square. rs. 3. rs\u20132744271"},{"key":"310_CR12","doi-asserted-by":"publisher","first-page":"4032","DOI":"10.1093\/brain\/awac297","volume":"145","author":"TJ Oberstein","year":"2022","unstructured":"Oberstein TJ, Schmidt MA, Florvaag A, Haas A-L, Siegmann E-M, Olm P, Utz J, Spitzer P, Doerfler A, Lewczuk P (2022) Amyloid-\u03b2 levels and cognitive trajectories in non-demented pTau181-positive subjects without amyloidopathy. Brain 145:4032\u20134041","journal-title":"Brain"},{"key":"310_CR13","doi-asserted-by":"publisher","first-page":"895","DOI":"10.14283\/jpad.2024.141","volume":"11","author":"RZ Zhou","year":"2024","unstructured":"Zhou RZ, Wimo A, Winblad B (2024) On the 2024 Alzheimer\u2019s association criteria: still not ready for clinical use. J Prev Alzheimer\u2019s Disease 11:895\u2013896","journal-title":"J Prev Alzheimer\u2019s Disease"},{"key":"310_CR14","doi-asserted-by":"crossref","unstructured":"Soladoye AA, Aderinto N, Osho D, Olawade DB (2025) Explainable machine learning models for early Alzheimer\u2019s disease detection using multimodal clinical data. Int J Med Informatics 106093","DOI":"10.1016\/j.ijmedinf.2025.106093"},{"key":"310_CR15","doi-asserted-by":"publisher","first-page":"1410544","DOI":"10.3389\/fnagi.2024.1410544","volume":"16","author":"X Wang","year":"2024","unstructured":"Wang X, Ye T, Jiang D, Zhou W, Zhang J, Initiative A (2024) s.D.N. Characterizing the clinical heterogeneity of early symptomatic Alzheimer\u2019s disease: A data-driven machine learning approach. Front Aging Neurosci 16:1410544","journal-title":"Front Aging Neurosci"},{"key":"310_CR16","doi-asserted-by":"publisher","first-page":"2201","DOI":"10.1007\/s13369-021-06131-3","volume":"47","author":"S Sava\u015f","year":"2022","unstructured":"Sava\u015f S (2022) Detecting the stages of Alzheimer\u2019s disease with pre-trained deep learning architectures. Arab J Sci Eng 47:2201\u20132218","journal-title":"Arab J Sci Eng"},{"key":"310_CR17","doi-asserted-by":"publisher","first-page":"S5","DOI":"10.2967\/jnumed.124.268756","volume":"66","author":"GD Rabinovici","year":"2025","unstructured":"Rabinovici GD, Knopman DS, Arbizu J, Benzinger TL, Donohoe KJ, Hansson O, Herscovitch P, Kuo PH, Lingler JH, Minoshima S (2025) Updated appropriate use criteria for amyloid and tau PET: a report from the Alzheimer\u2019s association and society for nuclear medicine and molecular imaging workgroup. J Nucl Med 66:S5\u2013S31","journal-title":"J Nucl Med"},{"key":"310_CR18","doi-asserted-by":"publisher","first-page":"1216215","DOI":"10.3389\/fnins.2023.1216215","volume":"17","author":"Q Zhao","year":"2023","unstructured":"Zhao Q, Du X, Chen W, Zhang T, Xu Z (2023) Advances in diagnosing mild cognitive impairment and Alzheimer\u2019s disease using 11\u00a0C-PIB-PET\/CT and common neuropsychological tests. Front NeuroSci 17:1216215","journal-title":"Front NeuroSci"},{"key":"310_CR19","doi-asserted-by":"crossref","unstructured":"Leon R, Ghahremani M, Guan DX, Smith EE, Zetterberg H, Ismail Z (2025) Enhancing alzheimer disease detection using neuropsychiatric symptoms: The role of mild behavioural impairment in the revised NIA-AA research framework. J Geriatr Psychiatr Neurol 08919887251366634","DOI":"10.1177\/08919887251366634"},{"key":"310_CR20","doi-asserted-by":"publisher","first-page":"e209753","DOI":"10.1212\/WNL.0000000000209753","volume":"103","author":"A Bieger","year":"2024","unstructured":"Bieger A, Brum WS, Borelli WV, Therriault J, De Bastiani MA, Moreira AG, Benedet AL, Ferrari-Souza JP, Da Costa JC, Souza DO (2024) Influence of different diagnostic criteria on Alzheimer disease clinical research. Neurology 103:e209753","journal-title":"Neurology"},{"key":"310_CR21","doi-asserted-by":"publisher","first-page":"e1276","DOI":"10.1212\/WNL.0000000000012598","volume":"97","author":"WS Eikelboom","year":"2021","unstructured":"Eikelboom WS, van den Berg E, Singleton EH, Baart SJ, Coesmans M, Leeuwis AE, Teunissen CE, van Berckel BN, Pijnenburg YA, Scheltens P (2021) Neuropsychiatric and cognitive symptoms across the Alzheimer disease clinical spectrum: cross-sectional and longitudinal associations. Neurology 97:e1276\u2013e1287","journal-title":"Neurology"},{"key":"310_CR22","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.3233\/JAD-221261","volume":"92","author":"J Shah","year":"2023","unstructured":"Shah J, Rahman Siddiquee MM, Krell-Roesch J, Syrjanen JA, Kremers WK, Vassilaki M, Forzani E, Wu T, Geda YE (2023) Neuropsychiatric symptoms and commonly used biomarkers of Alzheimer\u2019s disease: A literature review from a machine learning perspective. J Alzheimer\u2019s Disease 92:1131\u20131146","journal-title":"J Alzheimer\u2019s Disease"},{"key":"310_CR23","doi-asserted-by":"crossref","unstructured":"Dansereau C, Tam A, Badhwar A, Urchs S, Orban P, Rosa-Neto P, Bellec P (2017) A brain signature highly predictive of future progression to Alzheimer\u2019s dementia. arXiv preprint arXiv:1712.08058","DOI":"10.1016\/j.jalz.2017.06.2099"},{"key":"310_CR24","doi-asserted-by":"crossref","unstructured":"Tam A, Dansereau C, Iturria-Medina Y, Urchs S, Orban P, Sharmarke H, Breitner J, Bellec P, Initiative A (2019) s.D.N. A highly predictive signature of cognition and brain atrophy for progression to Alzheimer\u2019s dementia. Gigascience 8, giz055","DOI":"10.1093\/gigascience\/giz055"},{"key":"310_CR25","doi-asserted-by":"publisher","first-page":"e0314725","DOI":"10.1371\/journal.pone.0314725","volume":"19","author":"A Saeed","year":"2024","unstructured":"Saeed A, Waris A, Fuwad A, Iqbal J, Khan J, AlQahtani D, Gilani O, Shah UH, Initiative A (2024) s.D.N. Random survival forest model for early prediction of Alzheimer\u2019s disease conversion in early and late Mild cognitive impairment stages. PLoS ONE 19:e0314725","journal-title":"PLoS ONE"},{"key":"310_CR26","doi-asserted-by":"crossref","unstructured":"Yamada Y, Shinakwa K, Kobayashi M, Nemoto M, Ota M, Nemoto K, Arai T (2023) Smartwatch-derived Acoustic Markers for Deficits in Cognitively Relevant Everyday Functioning. In Proceedings of the IEEE International Conference on Digital Health (ICDH), 2023; pp. 39\u201349","DOI":"10.1109\/ICDH60066.2023.00015"},{"key":"310_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3709152","volume":"6","author":"N Bibi","year":"2025","unstructured":"Bibi N, Courtney J, McGuinness K (2025) Enhancing brain disease diagnosis with XAI: a review of recent studies. ACM Trans Comput Healthc 6:1\u201335","journal-title":"ACM Trans Comput Healthc"},{"key":"310_CR28","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s13550-021-00798-3","volume":"11","author":"S Kim","year":"2021","unstructured":"Kim S, Lee P, Oh KT, Byun MS, Yi D, Lee JH, Kim YK, Ye BS, Yun MJ, Lee DY (2021) Deep learning-based amyloid PET positivity classification model in the Alzheimer\u2019s disease continuum by using 2-[18F] FDG PET. EJNMMI Res 11:56","journal-title":"EJNMMI Res"},{"key":"310_CR29","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.3174\/ajnr.A6848","volume":"41","author":"C Suh","year":"2020","unstructured":"Suh C, Shim W, Kim S, Roh J, Lee J-H, Kim M-J, Park S, Jung W, Sung J, Jahng G-H (2020) Development and validation of a deep learning\u2013based automatic brain segmentation and classification algorithm for Alzheimer disease using 3D T1-weighted volumetric images. Am J Neuroradiol 41:2227\u20132234","journal-title":"Am J Neuroradiol"},{"key":"310_CR30","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s40708-024-00222-1","volume":"11","author":"V Vimbi","year":"2024","unstructured":"Vimbi V, Shaffi N, Mahmud M (2024) Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer\u2019s disease detection. Brain Inf 11:10","journal-title":"Brain Inf"},{"key":"310_CR31","doi-asserted-by":"publisher","first-page":"612","DOI":"10.3390\/diagnostics15050612","volume":"15","author":"M Taiyeb Khosroshahi","year":"2025","unstructured":"Taiyeb Khosroshahi M, Morsali S, Gharakhanlou S, Motamedi A, Hassanbaghlou S, Vahedi H, Pedrammehr S, Kabir HMD, Jafarizadeh A (2025) Explainable artificial intelligence in neuroimaging of Alzheimer\u2019s disease. Diagnostics 15:612","journal-title":"Diagnostics"},{"key":"310_CR32","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1:206\u2013215","journal-title":"Nat Mach Intell"},{"key":"310_CR33","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.neuroimage.2017.12.053","volume":"171","author":"MR Geddes","year":"2018","unstructured":"Geddes MR, Mattfeld AT, de Los Angeles C, Keshavan A, Gabrieli JD (2018) Human aging reduces the neurobehavioral influence of motivation on episodic memory. NeuroImage 171:296\u2013310","journal-title":"NeuroImage"},{"issue":"4","key":"310_CR34","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1002\/alz.13016","volume":"19","author":"M Better","year":"2023","unstructured":"Better M (2023) Alzheimer\u2019s disease facts and figures. Alzheimers Dement 19(4):1598\u20131695","journal-title":"Alzheimers Dement"},{"key":"310_CR35","doi-asserted-by":"publisher","first-page":"82","DOI":"10.3390\/bioengineering12010082","volume":"12","author":"D Coluzzi","year":"2025","unstructured":"Coluzzi D, Bordin V, Rivolta MW, Fortel I, Zhan L, Leow A, Baselli G (2025) Biomarker investigation using multiple brain measures from MRI through explainable artificial intelligence in Alzheimer\u2019s disease classification. Bioengineering 12:82","journal-title":"Bioengineering"},{"key":"310_CR36","doi-asserted-by":"crossref","unstructured":"Nigri E, Ziviani N, Cappabianco F, Antunes A, Veloso A (2020) Explainable deep CNNs for MRI-based diagnosis of Alzheimer\u2019s disease. In Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), ; pp. 1\u20138","DOI":"10.1109\/IJCNN48605.2020.9206837"},{"key":"310_CR37","unstructured":"Lozupone G, Bria A, Fontanella F, Meijer FJ, De Stefano CAXAIL (2024) Attention-based eXplainability for interpretable Alzheimer\u2019s localized diagnosis using 2D CNNs on 3D MRI brain scans. arXiv preprint arXiv:2407.02418"},{"key":"310_CR38","doi-asserted-by":"crossref","unstructured":"Anjomshoae S, Pudas S (2024) Explaining graph convolutional network predictions for clinicians: an explainable AI approach to Alzheimer\u2019s disease classification. Frontiers in Artificial Intelligence 6","DOI":"10.3389\/frai.2023.1334613"},{"key":"310_CR39","doi-asserted-by":"publisher","first-page":"1238065","DOI":"10.3389\/fnagi.2023.1238065","volume":"15","author":"N Amoroso","year":"2023","unstructured":"Amoroso N, Quarto S, La Rocca M, Tangaro S, Monaco A, Bellotti R (2023) An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer\u2019s disease. Front Aging Neurosci 15:1238065","journal-title":"Front Aging Neurosci"},{"key":"310_CR40","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/MSP.2021.3126573","volume":"39","author":"IB Galazzo","year":"2022","unstructured":"Galazzo IB, Cruciani F, Brusini L, Salih A, Radeva P, Storti SF, Menegaz G (2022) Explainable artificial intelligence for magnetic resonance imaging aging brainprints: Grounds and challenges. IEEE Signal Process Mag 39:99\u2013116","journal-title":"IEEE Signal Process Mag"},{"key":"310_CR41","doi-asserted-by":"publisher","first-page":"459","DOI":"10.3233\/JAD-230893","volume":"97","author":"R Bapat","year":"2024","unstructured":"Bapat R, Ma D, Duong TQ (2024) Predicting four-year\u2019s Alzheimer\u2019s disease onset using longitudinal neurocognitive tests and MRI data using explainable deep convolutional neural networks. J Alzheimer\u2019s Disease 97:459\u2013469","journal-title":"J Alzheimer\u2019s Disease"},{"key":"310_CR42","doi-asserted-by":"crossref","unstructured":"Dolci G, Cruciani F, Rahaman MA, Abrol A, Chen J, Fu Z, Galazzo IB, Menegaz G, Calhoun VD, Initiative A (2025) s.D.N. An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer\u2019s disease. ArXiv arXiv: 2406.13292 v13293.","DOI":"10.1088\/1741-2552\/ae087d"},{"key":"310_CR43","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40708-022-00165-5","volume":"9","author":"A Lombardi","year":"2022","unstructured":"Lombardi A, Diacono D, Amoroso N, Biecek P, Monaco A, Bellantuono L, Pantaleo E, Logroscino G, De Blasi R, Tangaro S (2022) A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of mild cognitive impairment and Alzheimer\u2019s disease. Brain Inf 9:17","journal-title":"Brain Inf"},{"key":"310_CR44","doi-asserted-by":"publisher","first-page":"2660","DOI":"10.1038\/s41598-021-82098-3","volume":"11","author":"S El-Sappagh","year":"2021","unstructured":"El-Sappagh S, Alonso JM, Islam SR, Sultan AM, Kwak KS (2021) A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer\u2019s disease. Sci Rep 11:2660","journal-title":"Sci Rep"},{"key":"310_CR45","unstructured":"Svenberg H, Explainable (2025) AI for Alzheimer\u2019s Prediction Diagnosis: Developing an Interpretable Deep Learning Pipeline with 3D Visualization"},{"key":"310_CR46","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s40846-023-00801-3","volume":"43","author":"M Odusami","year":"2023","unstructured":"Odusami M, Maskeli\u016bnas R, Dama\u0161evi\u010dius R, Misra S (2023) Explainable deep-learning-based diagnosis of Alzheimer\u2019s disease using multimodal input fusion of PET and MRI Images. J Med Biol Eng 43:291\u2013302","journal-title":"J Med Biol Eng"},{"key":"310_CR47","doi-asserted-by":"publisher","first-page":"345","DOI":"10.3390\/diagnostics14030345","volume":"14","author":"T Mahmud","year":"2024","unstructured":"Mahmud T, Barua K, Habiba SU, Sharmen N, Hossain MS, Andersson K (2024) An explainable ai paradigm for alzheimer\u2019s diagnosis using deep transfer learning. Diagnostics 14:345","journal-title":"Diagnostics"},{"key":"310_CR48","doi-asserted-by":"publisher","first-page":"8287","DOI":"10.3390\/app14188287","volume":"14","author":"KJ Junior","year":"2024","unstructured":"Junior KJ, Carole KS, Theodore Armand TP, Kim H-C, Initiative A (2024) s.D.N. Alzheimer\u2019s multiclassification using explainable AI techniques. Appl Sci 14:8287","journal-title":"Appl Sci"},{"key":"310_CR49","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s10278-022-00719-3","volume":"36","author":"LA De Santi","year":"2023","unstructured":"De Santi LA, Pasini E, Santarelli MF, Genovesi D, Positano V (2023) An explainable convolutional neural network for the early diagnosis of Alzheimer\u2019s disease from 18F-FDG PET. J Digit Imaging 36:189\u2013203","journal-title":"J Digit Imaging"},{"key":"310_CR50","doi-asserted-by":"crossref","unstructured":"Rasi R, Guvenis A, Initiative A.s.D.N. Subregional Biomarkers in FDG PET for Alzheimer\u2019s Diagnosis and Staging: An Interpretable and Explainable model. bioRxiv 2024, 2024.2012. 2027.630495","DOI":"10.1101\/2024.12.27.630495"},{"key":"310_CR51","doi-asserted-by":"publisher","first-page":"e053831","DOI":"10.1002\/alz.053831","volume":"17","author":"J Weatheritt","year":"2021","unstructured":"Weatheritt J, Palombit A, Manber R, Wolz R (2021) Alzheimer\u2019s disease detection using explainable AI on PET images. Alzheimer\u2019s Dement 17:e053831","journal-title":"Alzheimer\u2019s Dement"},{"key":"310_CR52","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.neurobiolaging.2024.08.002","volume":"143","author":"S Beer","year":"2024","unstructured":"Beer S, Elmenhorst D, Bischof GN, Ramirez A, Bauer A, Drzezga A, Initiative A (2024) s.D.N. Explainable artificial intelligence identifies an AQP4 polymorphism-based risk score associated with brain amyloid burden. Neurobiol Aging 143:19\u201329","journal-title":"Neurobiol Aging"},{"key":"310_CR53","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3389\/fnagi.2019.00194","volume":"11","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 11:194","journal-title":"Front Aging Neurosci"},{"key":"310_CR54","doi-asserted-by":"crossref","unstructured":"Li C, Jiao F, Wu S, Wang C, Wei M, Zhang S, Wang L, Huang Y, Yin Y, Tian R (2025) Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes with 18F-FDG PET imaging. Eur J Nucl Med Mol Imaging 1\u201318","DOI":"10.1007\/s00259-025-07478-7"},{"key":"310_CR55","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","journal-title":"Front Aging Neurosci"},{"key":"310_CR56","doi-asserted-by":"publisher","first-page":"fcac026","DOI":"10.1093\/braincomms\/fcac026","volume":"4","author":"W Pelkmans","year":"2022","unstructured":"Pelkmans W, Vromen EM, Dicks E, Scheltens P, Teunissen CE, Barkhof F, van der Flier WM, Tijms BM, Initiative A (2022) s.D.N. Grey matter network markers identify individuals with prodromal Alzheimer\u2019s disease who will show rapid clinical decline. Brain Commun 4:fcac026","journal-title":"Brain Commun"},{"key":"310_CR57","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.3233\/JAD-220021","volume":"87","author":"G Mirabnahrazam","year":"2022","unstructured":"Mirabnahrazam G, Ma D, Lee S, Popuri K, Lee H, Cao J, Wang L, Galvin JE, Beg MF, Initiative A (2022) s.D.N. Machine learning based multimodal neuroimaging genomics dementia score for predicting future conversion to alzheimer\u2019s disease. J Alzheimer\u2019s Disease 87:1345\u20131365","journal-title":"J Alzheimer\u2019s Disease"},{"key":"310_CR58","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1111\/jnc.15166","volume":"156","author":"J Diniz Pereira","year":"2021","unstructured":"Diniz Pereira J, Gomes Fraga V, Morais Santos AL, Carvalho MdG, Caramelli P, Braga Gomes K (2021) Alzheimer\u2019s disease and type 2 diabetes mellitus: A systematic review of proteomic studies. J Neurochem 156:753\u2013776","journal-title":"J Neurochem"},{"key":"310_CR59","doi-asserted-by":"publisher","first-page":"139","DOI":"10.21101\/cejph.a7238","volume":"30","author":"J Janoutov\u00e1","year":"2022","unstructured":"Janoutov\u00e1 J, Machaczka O, Zatloukalov\u00e1 A, Janout V (2022) Is Alzheimer\u2019s disease a type 3 diabetes? A review. Cent Eur J Public Health 30:139\u2013143","journal-title":"Cent Eur J Public Health"},{"key":"310_CR60","first-page":"1078","volume":"1863","author":"R Kandimalla","year":"2017","unstructured":"Kandimalla R, Thirumala V, Reddy PH (2017) Is Alzheimer\u2019s disease a type 3 diabetes? A critical appraisal. Biochim et Biophys Acta (BBA)-Molecular Basis Disease 1863:1078\u20131089","journal-title":"Biochim et Biophys Acta (BBA)-Molecular Basis Disease"},{"key":"310_CR61","doi-asserted-by":"publisher","first-page":"3165","DOI":"10.3390\/ijms21093165","volume":"21","author":"TT Nguyen","year":"2020","unstructured":"Nguyen TT, Ta QTH, Nguyen TKO, Nguyen TTD, Van Giau V (2020) Type 3 diabetes and its role implications in Alzheimer\u2019s disease. Int J Mol Sci 21:3165","journal-title":"Int J Mol Sci"},{"key":"310_CR62","doi-asserted-by":"crossref","unstructured":"Yamada Y, Kobayashi M, Shinkawa K, Nemoto M, Ota M, Nemoto K, Arai T (2022) Automated analysis of drawing process for detecting prodromal and clinical dementia. In Proceedings of the IEEE International Conference on Digital Health (ICDH), 2022; pp. 1\u20136","DOI":"10.1109\/ICDH55609.2022.00008"},{"key":"310_CR63","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1186\/s13195-023-01314-6","volume":"15","author":"B Dubois","year":"2023","unstructured":"Dubois B, von Arnim CA, Burnie N, Bozeat S, Cummings J (2023) Biomarkers in Alzheimer\u2019s disease: role in early and differential diagnosis and recognition of atypical variants. Alzheimers Res Ther 15:175","journal-title":"Alzheimers Res Ther"},{"key":"310_CR64","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.1002\/alz.12485","volume":"18","author":"A Benussi","year":"2022","unstructured":"Benussi A, Alberici A, Samra K, Russell LL, Greaves CV, Bocchetta M, Ducharme S, Finger E, Fumagalli G, Galimberti D (2022) Conceptual framework for the definition of preclinical and prodromal frontotemporal dementia. Alzheimer\u2019s Dement 18:1408\u20131423","journal-title":"Alzheimer\u2019s Dement"},{"key":"310_CR65","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.jalz.2019.04.001","volume":"15","author":"L Vermunt","year":"2019","unstructured":"Vermunt L, Sikkes SA, Van Den Hout A, Handels R, Bos I, Van Der Flier WM, Kern S, Ousset P-J, Maruff P, Skoog I (2019) Duration of preclinical, prodromal, and dementia stages of Alzheimer\u2019s disease in relation to age, sex, and APOE genotype. Alzheimer\u2019s Dement 15:888\u2013898","journal-title":"Alzheimer\u2019s Dement"},{"key":"310_CR66","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s13195-018-0459-7","volume":"11","author":"L Parnetti","year":"2019","unstructured":"Parnetti L, Chipi E, Salvadori N, D\u2019Andrea K, Eusebi P (2019) Prevalence and risk of progression of preclinical Alzheimer\u2019s disease stages: a systematic review and meta-analysis. Alzheimers Res Ther 11:7","journal-title":"Alzheimers Res Ther"},{"key":"310_CR67","doi-asserted-by":"publisher","first-page":"5143","DOI":"10.1002\/alz.13859","volume":"20","author":"CR Jack Jr","year":"2024","unstructured":"Jack Jr CR, Andrews JS, Beach TG, Buracchio T, Dunn B, Graf A, Hansson O, Ho C, Jagust W, McDade E (2024) Revised criteria for diagnosis and staging of Alzheimer\u2019s disease: Alzheimer\u2019s Association Workgroup. Alzheimer\u2019s Dement 20:5143\u20135169","journal-title":"Alzheimer\u2019s Dement"},{"key":"310_CR68","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1080\/13854046.2018.1523467","volume":"33","author":"A Geraldo","year":"2019","unstructured":"Geraldo A, Azeredo A, Pasion R, Dores AR, Barbosa F (2019) Fostering advances to neuropsychological assessment based on the Research Domain Criteria: The bridge between cognitive functioning and physiology. Clin Neuropsychol 33:327\u2013356","journal-title":"Clin Neuropsychol"},{"key":"310_CR69","doi-asserted-by":"crossref","unstructured":"Lorenzon G (2024) Brain heterogeneity within aging and cognitive impairment: implications for precision medicine and prevention. Karolinska Institutet","DOI":"10.69622\/27223515"},{"key":"310_CR70","unstructured":"Nami M, Mehrabi S, Kamali A-MK, Tahamtan M, Derman S, Kosagiaharaf R (2021) Spatial distribution of K-complexes in sleep quantitative-EEG among subjects with cogniform disorder. Scandinavia J Sleep Med 1"},{"key":"310_CR71","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40708-023-00195-7","volume":"10","author":"AD Arya","year":"2023","unstructured":"Arya AD, Verma SS, Chakarabarti P, Chakrabarti T, Elngar AA, Kamali A-M, Nami M (2023) A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer\u2019s disease. Brain Inf 10:17","journal-title":"Brain Inf"},{"key":"310_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-023-10192-x","volume":"16","author":"V Viswan","year":"2024","unstructured":"Viswan V, Shaffi N, Mahmud M, Subramanian K, Hajamohideen F (2024) Explainable artificial intelligence in Alzheimer\u2019s disease classification: A systematic review. Cogn Comput 16:1\u201344","journal-title":"Cogn Comput"},{"key":"310_CR73","doi-asserted-by":"publisher","first-page":"440","DOI":"10.3390\/diagnostics11030440","volume":"11","author":"F Ursin","year":"2021","unstructured":"Ursin F, Timmermann C, Steger F (2021) Ethical implications of Alzheimer\u2019s disease prediction in asymptomatic individuals through artificial intelligence. Diagnostics 11:440","journal-title":"Diagnostics"},{"key":"310_CR74","doi-asserted-by":"publisher","first-page":"1065904","DOI":"10.3389\/fnagi.2022.1065904","volume":"14","author":"C Clark","year":"2022","unstructured":"Clark C, Rabl M, Dayon L, Popp J (2022) The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer\u2019s disease. Front Aging Neurosci 14:1065904","journal-title":"Front Aging Neurosci"},{"key":"310_CR75","doi-asserted-by":"crossref","unstructured":"Natarajan S, Mathur S, Sidheekh S, Stammer W, Kersting K (2025) Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, ; pp. 28594\u201328600","DOI":"10.1609\/aaai.v39i27.35083"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-026-00310-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-026-00310-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-026-00310-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:46:18Z","timestamp":1782985578000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40708-026-00310-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,19]]},"references-count":75,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["310"],"URL":"https:\/\/doi.org\/10.1186\/s40708-026-00310-4","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,19]]},"assertion":[{"value":"28 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2026","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 authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"28"}}