{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:17:15Z","timestamp":1788239835135,"version":"build-2803163510"},"reference-count":633,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:00:00Z","timestamp":1787961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UID\/50016\/2025."],"award-info":[{"award-number":["UID\/50016\/2025."]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"award":["UID\/50016\/2025."],"award-info":[{"award-number":["UID\/50016\/2025."]}],"id":[{"id":"https:\/\/ror.org\/00snfqn58","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is the leading cause of dementia and a major cause of death worldwide, making early detection a critical clinical priority. Because pathological changes may begin 15\u201320 years before symptom onset, artificial intelligence (AI) has emerged as a promising tool for identifying and characterizing AD. In particular, multi-modal approaches that integrate cognitive, biological, and sensor-based data have attracted growing interest. This systematic review compares single- and multi-modal AI strategies for AD detection, covering machine learning and deep learning methods, feature representations, validation strategies, and classification tasks. Searches of major databases identified 568 studies published between 2016 and early 2026; 278 met the inclusion criteria according to PRISMA guidelines. Multi-modal approaches generally achieved higher performance than single-modal strategies, particularly for challenging tasks such as predicting progression between closely related disease stages, although direct comparisons under identical conditions remain scarce. Critically, only about 4% of studies evaluated their models on a genuinely independent external cohort, raising substantial concerns about model generalizability. Overall, current AI systems remain highly dependent on existing datasets and heterogeneous evaluation protocols, which limit generalizability and clinical applicability. Future research should prioritize representative multimodal datasets, rigorous external validation, and clinically interpretable AI systems.<\/jats:p>","DOI":"10.3390\/s26175489","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:55:56Z","timestamp":1788155756000},"page":"5489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer\u2019s Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0637-6482","authenticated-orcid":false,"given":"Jos\u00e9","family":"Menezes","sequence":"first","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina, Escola Superior de Biotecnologia, Portuguesa, Universidade Cat\u00f3lica Portuguesa, 4169-005 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9749-9674","authenticated-orcid":false,"given":"Maria In\u00eas","family":"Barbosa","sequence":"additional","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina, Escola Superior de Biotecnologia, Portuguesa, Universidade Cat\u00f3lica Portuguesa, 4169-005 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5381-6615","authenticated-orcid":false,"given":"Pedro Miguel","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"CBQF\u2014Centro de Biotecnologia e Qu\u00edmica Fina, Escola Superior de Biotecnologia, Portuguesa, Universidade Cat\u00f3lica Portuguesa, 4169-005 Porto, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2024). 2024 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 20, 3708\u20133821.","DOI":"10.1002\/alz.13809"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1111\/j.1471-4159.2006.04212.x","article-title":"Alois Alzheimer and Alzheimer\u2019s disease: A centennial perspective","volume":"99","author":"Small","year":"2006","journal-title":"J. Neurochem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13024-019-0333-5","article-title":"The neuropathological diagnosis of Alzheimer\u2019s disease","volume":"14","author":"DeTure","year":"2019","journal-title":"Mol. Neurodegener."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Breijyeh, Z., and Karaman, R. (2020). Comprehensive Review on Alzheimer\u2019s Disease: Causes and Treatment. Molecules, 25.","DOI":"10.3390\/molecules25245789"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.neurobiolaging.2017.06.027","article-title":"Identifying incipient dementia individuals using machine learning and amyloid imaging","volume":"59","author":"Mathotaarachchi","year":"2017","journal-title":"Neurobiol. Aging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/S0140-6736(24)01296-0","article-title":"Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission","volume":"404","author":"Livingston","year":"2024","journal-title":"Lancet"},{"key":"ref_7","first-page":"e70291","article-title":"Comparative methods for determining MoCA, MMSE, and CDR cut-offs in Alzheimer\u2019s disease: Insights from the ADNI study","volume":"18","author":"Atiya","year":"2026","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_8","first-page":"CD010775","article-title":"Montreal Cognitive Assessment for the detection of dementia","volume":"7","author":"Davis","year":"2021","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"423","DOI":"10.3233\/JAD-170991","article-title":"The Alzheimer\u2019s Disease Assessment Scale\u2013Cognitive Subscale (ADAS-Cog): Modifications and Responsiveness in Pre-Dementia Populations. A Narrative Review","volume":"63","author":"Kueper","year":"2018","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1038\/s41591-024-02869-z","article-title":"Highly accurate blood test for Alzheimer\u2019s disease is similar or superior to clinical cerebrospinal fluid tests","volume":"30","author":"Schindler","year":"2024","journal-title":"Nat. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1093\/clinchem\/hvad139","article-title":"Alzheimer Disease Biomarkers: Moving from CSF to Plasma for Reliable Detection of Amyloid and tau Pathology","volume":"69","author":"Kang","year":"2023","journal-title":"Clin. Chem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.4103\/1673-5374.367840","article-title":"Detection of Alzheimer\u2019s disease onset using MRI and PET neuroimaging: Longitudinal data analysis and machine learning","volume":"18","author":"Kulasiri","year":"2023","journal-title":"Neural Regen. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-022-00592-y","article-title":"Machine learning for medical imaging: Methodological failures and recommendations for the future","volume":"5","author":"Varoquaux","year":"2022","journal-title":"npj Digit. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): Clinical characterization","volume":"74","author":"Petersen","year":"2010","journal-title":"Neurology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2603","DOI":"10.1002\/alz.12640","article-title":"Screening and enrollment of underrepresented ethnocultural and educational populations in the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI)","volume":"18","author":"Ashford","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","article-title":"Convolutional neural networks for classification of Alzheimer\u2019s disease: Overview and reproducible evaluation","volume":"63","author":"Wen","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s13167-020-00216-z","article-title":"Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia\u2014Lessons for translation into clinical practice","volume":"11","author":"Birkenbihl","year":"2020","journal-title":"EPMA J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e105","DOI":"10.1016\/S2468-2667(21)00249-8","article-title":"Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019","volume":"7","author":"Nichols","year":"2022","journal-title":"Lancet Public Health"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fu, H., Huang, H., Liao, S., and Dai, Z. (2025). Global burden of Alzheimer\u2019s disease and other dementias (1990\u20132021): Inequality, frontier, and decomposition analysis. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1637029"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1056\/NEJMoa2310168","article-title":"Biomarker Changes during 20 Years Preceding Alzheimer\u2019s Disease","volume":"390","author":"Jia","year":"2024","journal-title":"N. Engl. J. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15056","DOI":"10.1038\/nrdp.2015.56","article-title":"Alzheimer\u2019s disease","volume":"1","author":"Masters","year":"2015","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/S1474-4422(09)70299-6","article-title":"Hypothetical model of dynamic biomarkers of the Alzheimer\u2019s pathological cascade","volume":"9","author":"Jack","year":"2010","journal-title":"Lancet Neurol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.jalz.2011.03.003","article-title":"Toward defining the preclinical stages of Alzheimer\u2019s disease: Recommendations from the National Institute on Aging\u2013Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"Sperling","year":"2011","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/S1474-4422(13)70044-9","article-title":"Amyloid \u03b2 deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer\u2019s disease: A prospective cohort study","volume":"12","author":"Villemagne","year":"2013","journal-title":"Lancet Neurol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1126\/science.1566067","article-title":"Alzheimer\u2019s Disease: The Amyloid Cascade Hypothesis","volume":"256","author":"Hardy","year":"1992","journal-title":"Science"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA Research Framework: Toward a biological definition of Alzheimer\u2019s disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_28","first-page":"888","article-title":"Preclinical Alzheimer Disease Drug Development: Early Considerations Based on Phase 3 Clinical Trials","volume":"26","author":"Hung","year":"2020","journal-title":"J. Manag. Care Spec. Pharm."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.jalz.2016.02.002","article-title":"Preclinical Alzheimer\u2019s disease: Definition, natural history, and diagnostic criteria","volume":"12","author":"Dubois","year":"2016","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.jalz.2014.01.001","article-title":"A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer\u2019s disease","volume":"10","author":"Jessen","year":"2014","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_31","first-page":"105","article-title":"From Subjective Cognitive Complaints to Dementia: Who Is at Risk?: A Systematic Review","volume":"31","author":"Alves","year":"2015","journal-title":"Am. J. Alzheimer\u2019s Dis. Other Dement."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s44202-022-00031-9","article-title":"Subjective memory complaints as a predictor of mild cognitive impairment and Alzheimer\u2019s disease","volume":"2","author":"Warren","year":"2022","journal-title":"Discov. Psychol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, S.Y., Lin, P.C., Lin, Y.C., Lee, Y.J., Wang, C.Y., Peng, S.W., and Wang, P.N. (2022). The Clinical Course of Early and Late Mild Cognitive Impairment. Front. Neurol., 13.","DOI":"10.3389\/fneur.2022.685636"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1001\/archneur.56.3.303","article-title":"Mild cognitive impairment: Clinical characterization and outcome","volume":"56","author":"Petersen","year":"1999","journal-title":"Arch. Neurol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1111\/j.1365-2796.2004.01388.x","article-title":"Mild cognitive impairment as a diagnostic entity","volume":"256","author":"Petersen","year":"2004","journal-title":"J. Intern. Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","article-title":"The diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease: Recommendations from the National Institute on Aging\u2013Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease","volume":"7","author":"Albert","year":"2011","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1111\/j.1600-0447.2008.01326.x","article-title":"Rate of progression of mild cognitive impairment to dementia\u2014Meta-analysis of 41 robust inception cohort studies","volume":"119","author":"Mitchell","year":"2009","journal-title":"Acta Psychiatr. Scand."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.jalz.2013.09.003","article-title":"Biological heterogeneity in ADNI amnestic mild cognitive impairment","volume":"10","author":"Nettiksimmons","year":"2014","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"275","DOI":"10.3233\/JAD-140276","article-title":"Neuropsychological Criteria for Mild Cognitive Impairment Improves Diagnostic Precision, Biomarker Associations, and Progression Rates","volume":"42","author":"Bondi","year":"2014","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7361","DOI":"10.1002\/alz.14167","article-title":"The Alzheimer\u2019s Disease Neuroimaging Initiative Clinical Core","volume":"20","author":"Aisen","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.neuroimage.2019.01.031","article-title":"A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer\u2019s disease","volume":"189","author":"Spasov","year":"2019","journal-title":"NeuroImage"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1038\/s41598-019-38793-3","article-title":"Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics","volume":"9","author":"Varatharajah","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Raza, M.L., Hassan, S.T., Jamil, S., Hyder, N., Batool, K., Walji, S., and Abbas, M.K. (2025). Advancements in deep learning for early diagnosis of Alzheimer\u2019s disease using multimodal neuroimaging: Challenges and future directions. Front. Neuroinform., 19.","DOI":"10.3389\/fninf.2025.1557177"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1056\/NEJMoa2212948","article-title":"Lecanemab in Early Alzheimer\u2019s Disease","volume":"388","author":"Swanson","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s41983-025-00968-3","article-title":"Amyloid solutions: Lecanemab, gantenerumab, and donanemab in the treatment of Alzheimer\u2019s disease","volume":"61","author":"Ameen","year":"2025","journal-title":"Egypt. J. Neurol. Psychiatry Neurosurg."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1007\/s10462-025-11258-y","article-title":"Alzheimer\u2019s disease detection using deep learning and machine learning: A review","volume":"58","author":"Mohsen","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gu, Z., Ge, B., Wang, Y., Gong, Y., and Qi, M. (2025). Artificial intelligence technologies for enhancing neurofunctionalities: A comprehensive review with applications in Alzheimer\u2019s disease research. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1609063"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1109\/42.712137","article-title":"MR image texture analysis applied to the diagnosis and tracking of Alzheimer\u2019s disease","volume":"17","author":"Freeborough","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"124780","DOI":"10.1016\/j.eswa.2024.124780","article-title":"Alzheimer\u2019s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions","volume":"255","author":"Elazab","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.neuroimage.2011.01.008","article-title":"Multimodal classification of Alzheimer\u2019s disease and mild cognitive impairment","volume":"55","author":"Zhang","year":"2011","journal-title":"NeuroImage"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3254","DOI":"10.1038\/s41598-020-74399-w","article-title":"Multimodal deep learning models for early detection of Alzheimer\u2019s disease stage","volume":"11","author":"Venugopalan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101848","DOI":"10.1016\/j.media.2020.101848","article-title":"Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review","volume":"67","author":"Ansart","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1016\/j.neuroimage.2017.03.057","article-title":"A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer\u2019s disease and its prodromal stages","volume":"155","author":"Rathore","year":"2017","journal-title":"NeuroImage"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jo, T., Nho, K., and Saykin, A.J. (2019). Deep Learning in Alzheimer\u2019s Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data. Front. Aging Neurosci., 11.","DOI":"10.3389\/fnagi.2019.00220"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.neuroimage.2011.06.029","article-title":"Effects of hardware heterogeneity on the performance of SVM Alzheimer\u2019s disease classifier","volume":"58","author":"Abdulkadir","year":"2011","journal-title":"NeuroImage"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.1016\/j.neurobiolaging.2013.06.015","article-title":"How early can we predict Alzheimer\u2019s disease using computational anatomy?","volume":"34","author":"Adaszewski","year":"2013","journal-title":"Neurobiol. Aging"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.pscychresns.2012.11.005","article-title":"Different multivariate techniques for automated classification of MRI data in Alzheimer\u2019s disease and mild cognitive impairment","volume":"212","author":"Aguilar","year":"2013","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Aidos, H., Duarte, J., and Fred, A. (2014). Identifying regions of interest for discriminating Alzheimer\u2019s disease from mild cognitive impairment. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP.2014.7025003"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Aksu, Y., Miller, D.J., Kesidis, G., Bigler, D.C., and Yang, Q.X. (2011). An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automatic Prognosis of MCI Patients. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0025074"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"464","DOI":"10.3390\/make6010024","article-title":"Alzheimer\u2019s Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review","volume":"6","author":"Alsubaie","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.mri.2011.11.001","article-title":"Partial least squares for discrimination in fMRI data","volume":"30","author":"Andersen","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.nicl.2013.12.012","article-title":"ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer\u2019s disease","volume":"4","author":"Apostolova","year":"2014","journal-title":"NeuroImage Clin."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Archana, M., and Ramakrishnan, S. (2014). Detection of Alzheimer disease in MR images using structure tensor. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE.","DOI":"10.1109\/EMBC.2014.6943772"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.acra.2007.10.020","article-title":"Automated Method for Identification of Patients with Alzheimer\u2019s Disease Based on Three-dimensional MR Images","volume":"15","author":"Arimura","year":"2008","journal-title":"Acad. Radiol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s12021-019-09417-y","article-title":"Fast and Precise Hippocampus Segmentation Through Deep Convolutional Neural Network Ensembles and Transfer Learning","volume":"17","author":"Ataloglou","year":"2019","journal-title":"Neuroinformatics"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3427","DOI":"10.1002\/hbm.25858","article-title":"The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects","volume":"43","author":"Balboni","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e240299","DOI":"10.1148\/ryai.240299","article-title":"DLMUSE: Robust Brain Segmentation in Seconds Using Deep Learning","volume":"7","author":"Bashyam","year":"2025","journal-title":"Radiol. Artif. Intell."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TMI.2011.2162961","article-title":"Generative-Discriminative Basis Learning for Medical Imaging","volume":"31","author":"Batmanghelich","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.compbiomed.2015.07.006","article-title":"Probability distribution function-based classification of structural MRI for the detection of Alzheimer\u2019s disease","volume":"64","author":"Beheshti","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_70","first-page":"40","article-title":"Classification of Alzheimer\u015b Disease from 3D Structural MRI Data","volume":"47","author":"Belmokhtar","year":"2012","journal-title":"Int. J. Comput. Appl."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1017\/S1041610202008281","article-title":"Volumetric MRI Measurements Can Differentiate Alzheimer\u2019s Disease, Mild Cognitive Impairment, and Normal Aging","volume":"14","author":"Bottino","year":"2002","journal-title":"Int. Psychogeriatr."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Cabral, C., and Silveira, M. (2013). Classification of Alzheimer\u2019s disease from FDG-PET images using favourite class ensembles. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC.2013.6610042"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.compbiomed.2015.01.003","article-title":"Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages","volume":"58","author":"Cabral","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1109\/TMI.2005.853923","article-title":"Large deformation diffeomorphic metric mapping of vector fields","volume":"24","author":"Cao","year":"2005","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e06226","DOI":"10.1016\/j.heliyon.2021.e06226","article-title":"Hippocampus segmentation on epilepsy and Alzheimer\u2019s disease studies with multiple convolutional neural networks","volume":"7","author":"Carmo","year":"2021","journal-title":"Heliyon"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Casanova, R., Whitlow, C.T., Wagner, B., Williamson, J., Shumaker, S.A., Maldjian, J.A., and Espeland, M.A. (2011). High Dimensional Classification of Structural MRI Alzheimer\u2019s Disease Data Based on Large Scale Regularization. Front. Neuroinform., 5.","DOI":"10.3389\/fninf.2011.00022"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Casanova, R., and Hsu, F.C. (2012). Classification of Structural MRI Images in Alzheimer\u2019s Disease from the Perspective of Ill-Posed Problems. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0044877"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.neuroimage.2015.02.037","article-title":"Gaussian process classification of Alzheimer\u2019s disease and mild cognitive impairment from resting-state fMRI","volume":"112","author":"Challis","year":"2015","journal-title":"NeuroImage"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"6047","DOI":"10.1088\/0031-9155\/56\/18\/017","article-title":"Efficient mining of association rules for the early diagnosis of Alzheimer\u2019s disease","volume":"56","author":"Chaves","year":"2011","journal-title":"Phys. Med. Biol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1016\/j.patrec.2012.04.011","article-title":"Functional brain image classification using association rules defined over discriminant regions","volume":"33","author":"Chaves","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"11766","DOI":"10.1016\/j.eswa.2012.04.075","article-title":"Association rule-based feature selection method for Alzheimer\u2019s disease diagnosis","volume":"39","author":"Chaves","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1016\/j.eswa.2012.09.003","article-title":"Integrating discretization and association rule-based classification for Alzheimer\u2019s disease diagnosis","volume":"40","author":"Chaves","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1148\/radiol.10100734","article-title":"Classification of Alzheimer Disease, Mild Cognitive Impairment, and Normal Cognitive Status with Large-Scale Network Analysis Based on Resting-State Functional MR Imaging","volume":"259","author":"Chen","year":"2011","journal-title":"Radiology"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1186\/1475-925X-12-S1-S2","article-title":"Development of a brain MRI-based hidden Markov model for dementia recognition","volume":"12","author":"Chen","year":"2013","journal-title":"BioMed. Eng. OnLine"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.jneumeth.2013.09.001","article-title":"Detecting brain structural changes as biomarker from magnetic resonance images using a local feature based SVM approach","volume":"221","author":"Chen","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"106953","DOI":"10.1016\/j.compbiomed.2023.106953","article-title":"RBS-Net: Hippocampus segmentation using multi-layer feature learning with the region, boundary and structure loss","volume":"160","author":"Chen","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Chen, F., Heng, T., Feng, Q., Hua, R., Wu, J., Shi, F., Liao, Z., Qiao, K., Zhang, Z., and Miao, J. (2025). Quantitative assessment of brain glymphatic imaging features using deep learning-based EPVS segmentation and DTI-ALPS analysis in Alzheimer\u2019s disease. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1621106"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"5125","DOI":"10.1007\/s00330-025-12251-3","article-title":"MRI-to-PET synthesis via deep learning for amyloid-\u03b2 quantification in Alzheimer\u2019s disease","volume":"36","author":"Chen","year":"2026","journal-title":"Eur. Radiol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1109\/TBME.2015.2404809","article-title":"Domain Transfer Learning for MCI Conversion Prediction","volume":"62","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1007\/s11682-015-9356-x","article-title":"Multimodal manifold-regularized transfer learning for MCI conversion prediction","volume":"9","author":"Cheng","year":"2015","journal-title":"Brain Imaging Behav."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.neuroimage.2011.05.083","article-title":"Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer\u2019s disease","volume":"58","author":"Chincarini","year":"2011","journal-title":"NeuroImage"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1016\/j.neuroimage.2011.09.085","article-title":"Individual subject classification for Alzheimer\u2019s disease based on incremental learning using a spatial frequency representation of cortical thickness data","volume":"59","author":"Cho","year":"2012","journal-title":"NeuroImage"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neuroimage.2011.11.066","article-title":"Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images","volume":"60","author":"Chu","year":"2012","journal-title":"NeuroImage"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.neucom.2013.01.065","article-title":"Evolutionary ELM wrapper feature selection for Alzheimer\u2019s disease CAD on anatomical brain MRI","volume":"128","author":"Chyzhyk","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neuroimage.2011.01.050","article-title":"Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment","volume":"56","author":"Costafreda","year":"2011","journal-title":"NeuroImage"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.nicl.2012.10.002","article-title":"Scoring by nonlocal image patch estimator for early detection of Alzheimer\u2019s disease","volume":"1","author":"Eskildsen","year":"2012","journal-title":"NeuroImage Clin."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1016\/j.neuroimage.2011.10.080","article-title":"Simultaneous segmentation and grading of anatomical structures for patient\u2019s classification: Application to Alzheimer\u2019s disease","volume":"59","author":"Eskildsen","year":"2012","journal-title":"NeuroImage"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Cui, Y., Liu, B., Luo, S., Zhen, X., Fan, M., Liu, T., Zhu, W., Park, M., Jiang, T., and Jin, J.S. (2011). Identification of Conversion from Mild Cognitive Impairment to Alzheimer\u2019s Disease Using Multivariate Predictors. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0021896"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1016\/j.neuroimage.2011.08.013","article-title":"Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: A combined spatial atrophy and white matter alteration approach","volume":"59","author":"Cui","year":"2012","journal-title":"NeuroImage"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1016\/j.neuroimage.2010.06.013","article-title":"Automatic classification of patients with Alzheimer\u2019s disease from structural MRI: A comparison of ten methods using the ADNI database","volume":"56","author":"Cuingnet","year":"2011","journal-title":"NeuroImage"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/TPAMI.2012.142","article-title":"Spatial and Anatomical Regularization of SVM: A General Framework for Neuroimaging Data","volume":"35","author":"Cuingnet","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1016\/j.neuroimage.2011.10.003","article-title":"Discriminative analysis of early Alzheimer\u2019s disease using multi-modal imaging and multi-level characterization with multi-classifier (M3)","volume":"59","author":"Dai","year":"2012","journal-title":"NeuroImage"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.neurobiolaging.2006.11.010","article-title":"Detection of prodromal Alzheimer\u2019s disease via pattern classification of magnetic resonance imaging","volume":"29","author":"Davatzikos","year":"2008","journal-title":"Neurobiol. Aging"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1016\/j.neuroimage.2008.03.050","article-title":"Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI","volume":"41","author":"Davatzikos","year":"2008","journal-title":"NeuroImage"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"2322.e19","DOI":"10.1016\/j.neurobiolaging.2010.05.023","article-title":"Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification","volume":"32","author":"Davatzikos","year":"2011","journal-title":"Neurobiol. Aging"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"119412","DOI":"10.1016\/j.neuroimage.2022.119412","article-title":"Deep learning based low-activity PET reconstruction of [11C]PiB and [18F]FE-PE2I in neurodegenerative disorders","volume":"259","author":"Daveau","year":"2022","journal-title":"NeuroImage"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0165-1781(95)02651-C","article-title":"Discriminant analysis of MRI measures as a method to determine the presence of dementia of the Alzheimer type","volume":"57","author":"DeCarli","year":"1995","journal-title":"Psychiatry Res."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"2048","DOI":"10.1093\/brain\/awp123","article-title":"Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer\u2019s disease","volume":"132","author":"Desikan","year":"2009","journal-title":"Brain"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Diciotti, S., Ginestroni, A., Bessi, V., Giannelli, M., Tessa, C., Bracco, L., Mascalchi, M., and Toschi, N. (2012). Identification of Mild Alzheimer\u2019s Disease through automated classification of structural MRI features. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE.","DOI":"10.1109\/EMBC.2012.6345959"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"118514","DOI":"10.1016\/j.neuroimage.2021.118514","article-title":"DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer\u2019s disease","volume":"243","author":"Dong","year":"2021","journal-title":"NeuroImage"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"imag-2-00294","DOI":"10.1162\/imag_a_00294","article-title":"Regional deep atrophy: Using temporal information to automatically identify regions associated with Alzheimer\u2019s disease progression from longitudinal MRI","volume":"2","author":"Dong","year":"2024","journal-title":"Imaging Neurosci."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Duarte, K.T.N., Sidhu, A.S., Barros, M.C., Gobbi, D.G., McCreary, C.R., Saad, F., Camicioli, R., Smith, E.E., Bento, M.P., and Frayne, R. (2024). Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation. Front. Comput. Neurosci., 18.","DOI":"10.3389\/fncom.2024.1487877"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TMI.2007.908685","article-title":"MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls","volume":"27","author":"Duchesne","year":"2008","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1016\/j.neurobiolaging.2008.09.003","article-title":"Amnestic MCI future clinical status prediction using baseline MRI features","volume":"31","author":"Duchesne","year":"2010","journal-title":"Neurobiol. Aging"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Dukart, J., Mueller, K., Horstmann, A., Barthel, H., M\u00f6ller, H.E., Villringer, A., Sabri, O., and Schroeter, M.L. (2011). Combined Evaluation of FDG-PET and MRI Improves Detection and Differentiation of Dementia. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0018111"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.pscychresns.2012.04.007","article-title":"Meta-analysis based SVM classification enables accurate detection of Alzheimer\u2019s disease across different clinical centers using FDG-PET and MRI","volume":"212","author":"Dukart","year":"2013","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Dyrba, M., Ewers, M., Wegrzyn, M., Kilimann, I., Plant, C., Oswald, A., Meindl, T., Pievani, M., Bokde, A.L.W., and Fellgiebel, A. (2013). Robust Automated Detection of Microstructural White Matter Degeneration in Alzheimer\u2019s Disease Using Machine Learning Classification of Multicenter DTI Data. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0064925"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2118","DOI":"10.1002\/hbm.22759","article-title":"Multimodal analysis of functional and structural disconnection in Alzheimer\u2019s disease using multiple kernel SVM","volume":"36","author":"Dyrba","year":"2015","journal-title":"Hum. Brain Mapp."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1002\/mrm.27201","article-title":"KIKI-net: Cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images","volume":"80","author":"Eo","year":"2018","journal-title":"Magn. Reson. Med."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Escudero, J., Zajicek, J.P., and Ifeachor, E. (2011). Machine Learning classification of MRI features of Alzheimer\u2019s disease and mild cognitive impairment subjects to reduce the sample size in clinical trials. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE.","DOI":"10.1109\/IEMBS.2011.6091962"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/TBME.2012.2212278","article-title":"Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer\u2019s Disease","volume":"60","author":"Escudero","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.neuroimage.2012.09.058","article-title":"Prediction of Alzheimer\u2019s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning","volume":"65","author":"Eskildsen","year":"2013","journal-title":"NeuroImage"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/j.neurobiolaging.2010.10.019","article-title":"Prediction of conversion from mild cognitive impairment to Alzheimer\u2019s disease dementia based upon biomarkers and neuropsychological test performance","volume":"33","author":"Ewers","year":"2012","journal-title":"Neurobiol. Aging"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.neuroimage.2008.02.043","article-title":"Structural and functional biomarkers of prodromal Alzheimer\u2019s disease: A high-dimensional pattern classification study","volume":"41","author":"Fan","year":"2008","journal-title":"NeuroImage"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1016\/j.neuroimage.2007.10.031","article-title":"Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline","volume":"39","author":"Fan","year":"2008","journal-title":"NeuroImage"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2014\/862307","article-title":"An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer\u2019s Disease: Classification Using Structural Features of Brain Images","volume":"2014","author":"Farhan","year":"2014","journal-title":"Comput. Math. Methods Med."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.bbr.2015.04.010","article-title":"Boosting diagnosis accuracy of Alzheimer\u2019s disease using high dimensional recognition of longitudinal brain atrophy patterns","volume":"290","author":"Farzan","year":"2015","journal-title":"Behav. Brain Res."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"643","DOI":"10.3233\/JAD-2009-1082","article-title":"Morphological Hippocampal Markers for Automated Detection of Alzheimer\u2019s Disease and Mild Cognitive Impairment Converters in Magnetic Resonance Images","volume":"17","author":"Ferrarini","year":"2009","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1002\/hbm.26165","article-title":"Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration","volume":"44","author":"Fu","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/JBHI.2019.2912659","article-title":"Deep Residual Inception Encoder\u2013Decoder Network for Medical Imaging Synthesis","volume":"24","author":"Gao","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.1109\/JBHI.2023.3304388","article-title":"Brain Status Transferring Generative Adversarial Network for Decoding Individualized Atrophy in Alzheimer\u2019s Disease","volume":"27","author":"Gao","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"5483","DOI":"10.1109\/JBHI.2023.3313392","article-title":"Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios","volume":"27","author":"Gende","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1016\/j.neuroimage.2009.05.036","article-title":"Multidimensional classification of hippocampal shape features discriminates Alzheimer\u2019s disease and mild cognitive impairment from normal aging","volume":"47","author":"Gerardin","year":"2009","journal-title":"NeuroImage"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Ghandian, S., Albarghouthi, L., Nava, K., Sharma, S.R.R., Minaud, L., Beckett, L., Saito, N., DeCarli, C., Rissman, R.A., and Teich, A.F. (2024). Learning precise segmentation of neurofibrillary tangles from rapid manual point annotations. bioRxiv.","DOI":"10.1101\/2024.05.15.594372"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.neuroscience.2015.08.013","article-title":"A novel method for early diagnosis of Alzheimer\u2019s disease based on pseudo Zernike moment from structural MRI","volume":"305","author":"Gorji","year":"2015","journal-title":"Neuroscience"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"865265","DOI":"10.1155\/2015\/865265","article-title":"Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer\u2019s Disease and Mild Cognitive Impairment","volume":"2015","author":"Goryawala","year":"2015","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1002\/hbm.24811","article-title":"Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks","volume":"41","author":"Goubran","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.neulet.2011.07.049","article-title":"Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson\u2019s correlation","volume":"502","author":"Termenon","year":"2011","journal-title":"Neurosci. Lett."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.nicl.2015.06.003","article-title":"A multi-contrast MRI study of microstructural brain damage in patients with mild cognitive impairment","volume":"8","author":"Granziera","year":"2015","journal-title":"NeuroImage Clin."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.neuroimage.2011.12.071","article-title":"Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer\u2019s disease","volume":"60","author":"Gray","year":"2012","journal-title":"NeuroImage"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.neuroimage.2012.09.065","article-title":"Random forest-based similarity measures for multi-modal classification of Alzheimer\u2019s disease","volume":"65","author":"Gray","year":"2013","journal-title":"NeuroImage"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1016\/j.jplph.2013.02.003","article-title":"Delayed ripening and improved fruit processing quality in tomato by RNAi-mediated silencing of three homologs of 1-aminopropane-1-carboxylate synthase gene","volume":"170","author":"Gupta","year":"2013","journal-title":"J. Plant Physiol."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neuroimage.2012.05.022","article-title":"Multi-scale classification of disease using structural MRI and wavelet transform","volume":"62","author":"Hackmack","year":"2012","journal-title":"NeuroImage"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"315","DOI":"10.3233\/JAD-2010-100840","article-title":"Individual Prediction of Cognitive Decline in Mild Cognitive Impairment Using Support Vector Machine-Based Analysis of Diffusion Tensor Imaging Data","volume":"22","author":"Haller","year":"2010","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"283","DOI":"10.3174\/ajnr.A3223","article-title":"Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI","volume":"34","author":"Haller","year":"2012","journal-title":"Am. J. Neuroradiol."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1007\/s10278-022-00613-y","article-title":"Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI)","volume":"35","author":"Hazarika","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"2407","DOI":"10.1093\/cercor\/bhl149","article-title":"Small-World Anatomical Networks in the Human Brain Revealed by Cortical Thickness from MRI","volume":"17","author":"He","year":"2007","journal-title":"Cereb. Cortex"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Hidalgo-Mu\u00f1oz, A.R., Ram\u00edrez, J., G\u00f3rriz, J.M., and Padilla, P. (2014). Regions of interest computed by SVM wrapped method for Alzheimer\u2019s disease examination from segmented MRI. Front. Aging Neurosci., 6.","DOI":"10.3389\/fnagi.2014.00020"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.neuroimage.2009.05.056","article-title":"Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset","volume":"48","author":"Hinrichs","year":"2009","journal-title":"NeuroImage"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Hinrichs, C., Singh, V., Xu, G., and Johnson, S. (2009). MKL for Robust Multi-modality AD Classification. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2009, Springer.","DOI":"10.1007\/978-3-642-04271-3_95"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.neuroimage.2010.10.081","article-title":"Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population","volume":"55","author":"Hinrichs","year":"2011","journal-title":"NeuroImage"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.artmed.2009.05.001","article-title":"Differential automatic diagnosis between Alzheimer\u2019s disease and frontotemporal dementia based on perfusion SPECT images","volume":"47","author":"Horn","year":"2009","journal-title":"Artif. Intell. Med."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Huang, C., Yan, B., Jiang, H., and Wang, D. (2008). Combining Voxel-based Morphometry with Artifical Neural Network Theory in the Application Research of Diagnosing Alzheimer\u2019s Disease. Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics, IEEE.","DOI":"10.1109\/BMEI.2008.245"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1002\/hbm.26109","article-title":"Semisupervised white matter hyperintensities segmentation on MRI","volume":"44","author":"Huang","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1093\/jnen\/nlae048","article-title":"Automated deep learning segmentation of neuritic plaques and neurofibrillary tangles in Alzheimer disease brain sections using a proprietary software","volume":"83","author":"Ingrassia","year":"2024","journal-title":"J. Neuropathol. Exp. Neurol."},{"key":"ref_156","unstructured":"Islam, J., and Zhang, Y. (2017). An Ensemble of Deep Convolutional Neural Networks for Alzheimer\u2019s Disease Detection and Classification. arXiv."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s40708-020-00104-2","article-title":"GAN-based synthetic brain PET image generation","volume":"7","author":"Islam","year":"2020","journal-title":"Brain Inform."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/TRPMS.2025.3581204","article-title":"A Cross-Modality Transformer Network for MR-Guided Low-Dose Tau PET Image Denoising","volume":"10","author":"Jang","year":"2026","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"4825","DOI":"10.1038\/s41598-021-84358-8","article-title":"Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework","volume":"11","author":"Jeong","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_160","unstructured":"Jiang, Q., and Shi, J. (2014). Sparse kernel entropy component analysis for dimensionality reduction of neuroimaging data. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Jie, B., Zhang, D., Cheng, B., and Shen, D. (2013). Manifold Regularized Multi-Task Feature Selection for Multi-Modality Classification in Alzheimer\u2019s Disease. Advanced Information Systems Engineering, Springer.","DOI":"10.1007\/978-3-642-40811-3_35"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.1002\/hbm.22353","article-title":"Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification: Topological Graph Kernel","volume":"35","author":"Jie","year":"2013","journal-title":"Hum. Brain Mapp."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1109\/TBME.2013.2284195","article-title":"Integration of Network Topological and Connectivity Properties for Neuroimaging Classification","volume":"61","author":"Jie","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1002\/hbm.22642","article-title":"Manifold regularized multitask feature learning for multimodality disease classification","volume":"36","author":"Jie","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"92","DOI":"10.4306\/pi.2015.12.1.92","article-title":"Automated Classification to Predict the Progression of Alzheimer\u2019s Disease Using Whole-Brain Volumetry and DTI","volume":"12","author":"Jung","year":"2015","journal-title":"Psychiatry Investig."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"3769","DOI":"10.1002\/hbm.24210","article-title":"Adaptive template generation for amyloid PET using a deep learning approach","volume":"39","author":"Kang","year":"2018","journal-title":"Hum. Brain Mapp."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"659","DOI":"10.2967\/jnumed.122.264414","article-title":"Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks","volume":"64","author":"Kang","year":"2022","journal-title":"J. Nucl. Med."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1007\/s13139-024-00861-6","article-title":"Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method","volume":"58","author":"Kang","year":"2024","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1212\/WNL.48.4.978","article-title":"Midline cerebral morphometry distinguishes frontotemporal dementia and Alzheimer\u2019s disease","volume":"48","author":"Kaufer","year":"1997","journal-title":"Neurology"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1016\/j.clinph.2015.02.060","article-title":"Identifying patients with Alzheimer\u2019s disease using resting-state fMRI and graph theory","volume":"126","author":"Khazaee","year":"2015","journal-title":"Clin. Neurophysiol."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.2967\/jnumed.123.267360","article-title":"Improving18F-FDG PET Quantification Through a Spatial Normalization Method","volume":"65","author":"Kim","year":"2024","journal-title":"J. Nucl. Med."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"939","DOI":"10.3233\/JAD-150334","article-title":"Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study","volume":"47","author":"Peter","year":"2015","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1093\/brain\/awm319","article-title":"Automatic classification of MR scans in Alzheimer\u2019s disease","volume":"131","author":"Kloppel","year":"2008","journal-title":"Brain"},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/j.neurobiolaging.2010.04.022","article-title":"Boosting power for clinical trials using classifiers based on multiple biomarkers","volume":"31","author":"Kohannim","year":"2010","journal-title":"Neurobiol. Aging"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1016\/j.neuroimage.2011.03.029","article-title":"Multi-template tensor-based morphometry: Application to analysis of Alzheimer\u2019s disease","volume":"56","author":"Koikkalainen","year":"2011","journal-title":"NeuroImage"},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Koikkalainen, J., P\u00f6l\u00f6nen, H., Mattila, J., van Gils, M., Soininen, H., and L\u00f6tj\u00f6nen, J. (2012). Improved Classification of Alzheimer\u2019s Disease Data via Removal of Nuisance Variability. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0031112"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1049\/htl.2013.0022","article-title":"New approach for automatic classification of Alzheimer\u2019s disease, mild cognitive impairment and healthy brain magnetic resonance images","volume":"1","author":"Lahmiri","year":"2014","journal-title":"Healthc. Technol. Lett."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"49064","DOI":"10.1109\/ACCESS.2022.3171927","article-title":"BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis","volume":"10","author":"Lai","year":"2022","journal-title":"IEEE Access"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1017\/S1461145711000459","article-title":"Identification of a blood-based biomarker panel for classification of Alzheimer\u2019s disease","volume":"14","author":"Laske","year":"2011","journal-title":"Int. J. Neuropsychopharmacol."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1016\/j.compbiomed.2013.07.004","article-title":"Classification of diffusion tensor images for the early detection of Alzheimer\u2019s disease","volume":"43","author":"Lee","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"61","DOI":"10.4258\/hir.2014.20.1.61","article-title":"Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis","volume":"20","author":"Lee","year":"2014","journal-title":"Healthc. Inform. Res."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neurobiolaging.2006.09.013","article-title":"Automated cortical thickness measurements from MRI can accurately separate Alzheimer\u2019s patients from normal elderly controls","volume":"29","author":"Lerch","year":"2008","journal-title":"Neurobiol. Aging"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.3174\/ajnr.A0620","article-title":"Hippocampal Shape Analysis of Alzheimer Disease Based on Machine Learning Methods","volume":"28","author":"Li","year":"2007","journal-title":"Am. J. Neuroradiol."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"427.e15","DOI":"10.1016\/j.neurobiolaging.2010.11.008","article-title":"Discriminant analysis of longitudinal cortical thickness changes in Alzheimer\u2019s disease using dynamic and network features","volume":"33","author":"Li","year":"2012","journal-title":"Neurobiol. Aging"},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Li, M., Oishi, K., He, X., Qin, Y., Gao, F., and Mori, S. (2014). An Efficient Approach for Differentiating Alzheimer\u2019s Disease from Normal Elderly Based on Multicenter MRI Using Gray-Level Invariant Features. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105563"},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1016\/j.mri.2014.05.008","article-title":"Discriminative analysis of multivariate features from structural MRI and diffusion tensor images","volume":"32","author":"Li","year":"2014","journal-title":"Magn. Reson. Imaging"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"10541","DOI":"10.1523\/JNEUROSCI.4356-13.2014","article-title":"Abnormal Changes of Multidimensional Surface Features Using Multivariate Pattern Classification in Amnestic Mild Cognitive Impairment Patients","volume":"34","author":"Li","year":"2014","journal-title":"J. Neurosci."},{"key":"ref_188","doi-asserted-by":"crossref","unstructured":"Li, H., Liu, Y., Gong, P., Zhang, C., and Ye, J. (2014). Hierarchical Interactions Model for Predicting Mild Cognitive Impairment (MCI) to Alzheimer\u2019s Disease (AD) Conversion. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0082450"},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1109\/JBHI.2015.2429556","article-title":"A Robust Deep Model for Improved Classification of AD\/MCI Patients","volume":"19","author":"Li","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Lillemark, L., S\u00f8rensen, L., Pai, A., Dam, E.B., and Nielsen, M. (2014). Brain region\u2019s relative proximity as marker for Alzheimer\u2019s disease based on structural MRI. BMC Med. Imaging, 14.","DOI":"10.1186\/1471-2342-14-21"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1016\/j.neuroimage.2012.01.055","article-title":"Ensemble sparse classification of Alzheimer\u2019s disease","volume":"60","author":"Liu","year":"2012","journal-title":"NeuroImage"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.neuroimage.2013.06.033","article-title":"Locally linear embedding (LLE) for MRI based Alzheimer\u2019s disease classification","volume":"83","author":"Liu","year":"2013","journal-title":"NeuroImage"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1002\/hbm.22254","article-title":"Hierarchical fusion of features and classifier decisions for Alzheimer\u2019s disease diagnosis","volume":"35","author":"Liu","year":"2013","journal-title":"Hum. Brain Mapp."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.neuroimage.2013.09.015","article-title":"Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer\u2019s Disease and mild cognitive impairment identification","volume":"84","author":"Liu","year":"2014","journal-title":"NeuroImage"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1109\/JBHI.2013.2285378","article-title":"Multiple Kernel Learning in the Primal for Multimodal Alzheimer\u2019s Disease Classification","volume":"18","author":"Liu","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_196","doi-asserted-by":"crossref","unstructured":"Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D. (2014). Early diagnosis of Alzheimer\u2019s disease with deep learning. Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), IEEE.","DOI":"10.1109\/ISBI.2014.6868045"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","article-title":"Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer\u2019s Disease","volume":"62","author":"Liu","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1002\/hbm.22741","article-title":"View-centralized multi-atlas classification for Alzheimer\u2019s disease diagnosis","volume":"36","author":"Liu","year":"2015","journal-title":"Hum. Brain Mapp."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"1","DOI":"10.26599\/BDMA.2018.9020001","article-title":"Applications of deep learning to MRI images: A survey","volume":"1","author":"Liu","year":"2018","journal-title":"Big Data Min. Anal."},{"key":"ref_200","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Yan, Z. (2020). A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI. Sensors, 20.","DOI":"10.3390\/s20133628"},{"key":"ref_201","doi-asserted-by":"crossref","unstructured":"Liu, X., Marin, T., Eslahi, S.V., Tiss, A., Chemli, Y., Johson, K.A., Fakhri, G.E., and Ouyang, J. (2024). Subject-aware PET Denoising with Contrastive Adversarial Domain Generalization. Proceedings of the 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), IEEE.","DOI":"10.1109\/NSS\/MIC\/RTSD57108.2024.10656150"},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"085001","DOI":"10.1088\/1361-6560\/ad341a","article-title":"Cross noise level PET denoising with continuous adversarial domain generalization","volume":"69","author":"Liu","year":"2024","journal-title":"Phys. Med. Biol."},{"key":"ref_203","doi-asserted-by":"crossref","unstructured":"Long, X., and Wyatt, C. (2010). An automatic unsupervised classification of MR images in Alzheimer\u2019s disease. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2010.5540031"},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Lopez, M., Ramirez, J., Gorriz, J.M., Salas-Gonzalez, D., Alvarez, I., Segovia, F., and Chaves, R. (2009). Neurological image classification for the Alzheimer\u2019s Disease diagnosis using Kernel PCA and Support Vector Machines. Proceedings of the 2009 IEEE Nuclear Science Symposium Conference Record (NSS\/MIC), IEEE.","DOI":"10.1109\/NSSMIC.2009.5402069"},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1049\/el.2009.0176","article-title":"Automatic tool for Alzheimer\u2019s disease diagnosis using PCA and Bayesian classification rules","volume":"45","author":"Segovia","year":"2009","journal-title":"Electron. Lett."},{"key":"ref_206","first-page":"17","article-title":"Selecting and Extracting Effective Features for Automated Diagnosis of Alzheimer\u015b Disease","volume":"81","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s00234-008-0463-x","article-title":"Support vector machine-based classification of Alzheimer\u2019s disease from whole-brain anatomical MRI","volume":"51","author":"Magnin","year":"2008","journal-title":"Neuroradiology"},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.neunet.2012.02.035","article-title":"Identification of brain regions responsible for Alzheimer\u2019s disease using a Self-adaptive Resource Allocation Network","volume":"32","author":"Mahanand","year":"2012","journal-title":"Neural Netw."},{"key":"ref_209","doi-asserted-by":"crossref","unstructured":"Mahmood, R., and Ghimire, B. (2013). Automatic detection and classification of Alzheimer\u2019s Disease from MRI scans using principal component analysis and artificial neural networks. Proceedings of the 2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP), IEEE.","DOI":"10.1109\/IWSSIP.2013.6623471"},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1038\/s41598-022-05287-8","article-title":"A novel deep learning based hippocampus subfield segmentation method","volume":"12","author":"Romero","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_211","first-page":"961314","article-title":"Improved Diagnostic Multimodal Biomarkers for Alzheimer\u2019s Disease and Mild Cognitive Impairment","volume":"2015","year":"2015","journal-title":"BioMed Res. Int."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1148\/radiol.2511080924","article-title":"Alzheimer Disease: Quantitative Structural Neuroimaging for Detection and Prediction of Clinical and Structural Changes in Mild Cognitive Impairment","volume":"251","author":"McEvoy","year":"2009","journal-title":"Radiology"},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1148\/radiol.11101975","article-title":"Mild Cognitive Impairment: Baseline and Longitudinal Structural MR Imaging Measures Improve Predictive Prognosis","volume":"259","author":"McEvoy","year":"2011","journal-title":"Radiology"},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1002\/hbm.20655","article-title":"Collaborative computational anatomy: An MRI morphometry study of the human brain via diffeomorphic metric mapping","volume":"30","author":"Miller","year":"2008","journal-title":"Hum. Brain Mapp."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"5052","DOI":"10.1002\/hbm.22531","article-title":"Multi-atlas based representations for Alzheimer\u2019s disease diagnosis","volume":"35","author":"Min","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1016\/j.neuroimage.2008.10.031","article-title":"Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI","volume":"44","author":"Misra","year":"2009","journal-title":"NeuroImage"},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"3255","DOI":"10.1109\/JSEN.2013.2263794","article-title":"Enhanced Compressibility of EEG Signal in Alzheimer\u2019s Disease Patients","volume":"13","author":"Morabito","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.neuroimage.2014.10.002","article-title":"Machine learning framework for early MRI-based Alzheimer\u2019s conversion prediction in MCI subjects","volume":"104","author":"Moradi","year":"2015","journal-title":"NeuroImage"},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1002\/hbm.20934","article-title":"Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer\u2019s disease","volume":"31","author":"Mueller","year":"2010","journal-title":"Hum. Brain Mapp."},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"S132","DOI":"10.1016\/j.neurobiolaging.2014.05.037","article-title":"Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer\u2019s disease","volume":"36","author":"Nir","year":"2015","journal-title":"Neurobiol. Aging"},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"106294","DOI":"10.1016\/j.compbiomed.2022.106294","article-title":"P-ResUnet: Segmentation of brain tissue with Purified Residual Unet","volume":"151","author":"Niu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_222","doi-asserted-by":"crossref","unstructured":"Nobakht, S., Schaeffer, M., Forkert, N.D., Nestor, S., Black, S.E., and Barber, P. (2021). Combined Atlas and Convolutional Neural Network-Based Segmentation of the Hippocampus from MRI According to the ADNI Harmonized Protocol. Sensors, 21.","DOI":"10.3390\/s21072427"},{"key":"ref_223","doi-asserted-by":"crossref","unstructured":"ODwyer, L., Lamberton, F., Bokde, A.L.W., Ewers, M., Faluyi, Y.O., Tanner, C., Mazoyer, B., ONeill, D., Bartley, M., and Collins, D.R. (2012). Using Support Vector Machines with Multiple Indices of Diffusion for Automated Classification of Mild Cognitive Impairment. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0032441"},{"key":"ref_224","doi-asserted-by":"crossref","unstructured":"Oishi, K., Akhter, K., Mielke, M., Ceritoglu, C., Zhang, J., Jiang, H., Li, X., Younes, L., Miller, M.I., and van Zijl, P.C.M. (2011). Multi-Modal MRI Analysis with Disease-Specific Spatial Filtering: Initial Testing to Predict Mild Cognitive Impairment Patients Who Convert to Alzheimer\u2019s Disease. Front. Neurol., 2.","DOI":"10.3389\/fneur.2011.00054"},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"e2509321123","DOI":"10.1073\/pnas.2509321123","article-title":"Probabilistic mapping and automated segmentation of human brainstem white matter bundles","volume":"123","author":"Olchanyi","year":"2026","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.3233\/JAD-2010-1322","article-title":"Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer\u2019s Disease","volume":"19","author":"Oliveira","year":"2010","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1093\/jnen\/nlac132","article-title":"Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-\u03b2 analyses on digitized human brain slides","volume":"82","author":"Oliveira","year":"2023","journal-title":"J. Neuropathol. Exp. Neurol."},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1016\/j.patrec.2013.04.014","article-title":"LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer\u2019s disease","volume":"34","author":"Ortiz","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_229","doi-asserted-by":"crossref","unstructured":"Ortiz, A., G\u00f3rriz, J.M., Ram\u00edrez, J., and Martinez-Murcia, F.J. (2014). Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0093851"},{"key":"ref_230","doi-asserted-by":"crossref","unstructured":"Ortiz, A., Munilla, J., \u00c1lvarez Ill\u00e1n, I., G\u00f3rriz, J.M., and Ram\u00edrez, J. (2015). Exploratory graphical models of functional and structural connectivity patterns for Alzheimer\u2019s Disease diagnosis. Front. Comput. Neurosci., 9.","DOI":"10.3389\/fncom.2015.00132"},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.jneumeth.2013.10.003","article-title":"A comparison of three brain atlases for MCI prediction","volume":"221","author":"Ota","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.jneumeth.2015.08.020","article-title":"Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer\u2019s disease","volume":"256","author":"Ota","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"3555","DOI":"10.1002\/mp.13626","article-title":"Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss","volume":"46","author":"Ouyang","year":"2019","journal-title":"Med. Phys."},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/TMI.2011.2167628","article-title":"NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer\u2019s Disease","volume":"31","author":"Padilla","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.nicl.2014.11.007","article-title":"Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer\u2019s disease from healthy controls. A European Alzheimer\u2019s Disease Consortium (EADC) study","volume":"7","author":"Pagani","year":"2015","journal-title":"NeuroImage Clin."},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.neucom.2014.02.076","article-title":"A lattice computing approach to Alzheimer\u2019s disease computer assisted diagnosis based on MRI data","volume":"150","author":"Papakostas","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.neulet.2012.09.011","article-title":"Dimensionality reduced cortical features and their use in the classification of Alzheimer\u2019s disease and mild cognitive impairment","volume":"529","author":"Park","year":"2012","journal-title":"Neurosci. Lett."},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.neulet.2013.06.042","article-title":"Dimensionality reduced cortical features and their use in predicting longitudinal changes in Alzheimer\u2019s disease","volume":"550","author":"Park","year":"2013","journal-title":"Neurosci. Lett."},{"key":"ref_239","doi-asserted-by":"crossref","first-page":"109410","DOI":"10.1016\/j.compbiomed.2024.109410","article-title":"Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils","volume":"184","author":"Park","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s10439-024-03606-w","article-title":"Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks","volume":"53","author":"Patel","year":"2024","journal-title":"Ann. Biomed. Eng."},{"key":"ref_241","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0197-4580(03)00084-8","article-title":"Hippocampus and entorhinal cortex in mild cognitive impairment and early AD","volume":"25","author":"Pennanen","year":"2004","journal-title":"Neurobiol. Aging"},{"key":"ref_242","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.neuroimage.2009.11.046","article-title":"Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer\u2019s disease","volume":"50","author":"Plant","year":"2010","journal-title":"NeuroImage"},{"key":"ref_243","doi-asserted-by":"crossref","unstructured":"Poil, S.S., de Haan, W., van der Flier, W.M., Mansvelder, H.D., Scheltens, P., and Linkenkaer-Hansen, K. (2013). Integrative EEG biomarkers predict progression to Alzheimer\u2019s disease at the MCI stage. Front. Aging Neurosci., 5.","DOI":"10.3389\/fnagi.2013.00058"},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"014003","DOI":"10.1117\/1.JMI.11.1.014003","article-title":"Attention-gated 3D CapsNet for robust hippocampal segmentation","volume":"11","author":"Poiret","year":"2024","journal-title":"J. Med. Imaging"},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"789","DOI":"10.2174\/156720512802455359","article-title":"Computer based Classification of MR Scans in First Time Applicant Alzheimer Patients","volume":"9","author":"Polat","year":"2012","journal-title":"Curr. Alzheimer Res."},{"key":"ref_246","doi-asserted-by":"crossref","unstructured":"Polikar, R., Tilley, C., Hillis, B., and Clark, C.M. (2010). Multimodal EEG, MRI and PET data fusion for Alzheimer\u2019s disease diagnosis. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE.","DOI":"10.1109\/IEMBS.2010.5627621"},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"S121","DOI":"10.1016\/j.neurobiolaging.2014.04.037","article-title":"Brain connectivity and novel network measures for Alzheimer\u2019s disease classification","volume":"36","author":"Prasad","year":"2015","journal-title":"Neurobiol. Aging"},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neuroimage.2007.11.041","article-title":"Parallel transport in diffeomorphisms distinguishes the time-dependent pattern of hippocampal surface deformation due to healthy aging and the dementia of the Alzheimer\u2019s type","volume":"40","author":"Qiu","year":"2008","journal-title":"NeuroImage"},{"key":"ref_249","doi-asserted-by":"crossref","first-page":"2036","DOI":"10.1093\/brain\/awp105","article-title":"Early diagnosis of Alzheimer\u2019s disease using cortical thickness: Impact of cognitive reserve","volume":"132","author":"Querbes","year":"2009","journal-title":"Brain"},{"key":"ref_250","first-page":"29","article-title":"Imputation And Classification Of Missing Data Using Least Square Support Vector Machines\u2014A New Approach In Dementia Diagnosis","volume":"1","author":"Sivapriya","year":"2012","journal-title":"Int. J. Adv. Res. Artif. Intell."},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.nicl.2014.09.005","article-title":"Novel ThickNet features for the discrimination of amnestic MCI subtypes","volume":"6","author":"Raamana","year":"2014","journal-title":"NeuroImage Clin."},{"key":"ref_252","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.neulet.2010.01.056","article-title":"Computer aided diagnosis system for the Alzheimer\u2019s disease based on partial least squares and random forest SPECT image classification","volume":"472","author":"Segovia","year":"2010","journal-title":"Neurosci. Lett."},{"key":"ref_253","doi-asserted-by":"crossref","unstructured":"Rangini, M., and Jiji, G.W. (2013). Detection of Alzheimer\u2019s disease through automated hippocampal segmentation. Proceedings of the 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), IEEE.","DOI":"10.1109\/iMac4s.2013.6526397"},{"key":"ref_254","doi-asserted-by":"crossref","unstructured":"Rao, A., Lee, Y., Gass, A., and Monsch, A. (2011). Classification of Alzheimer\u2019s Disease from structural MRI using sparse logistic regression with optional spatial regularization. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE.","DOI":"10.1109\/IEMBS.2011.6091115"},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"102257","DOI":"10.1016\/j.media.2021.102257","article-title":"Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia","volume":"75","author":"Ravi","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_256","doi-asserted-by":"crossref","unstructured":"Ravi, K.S., Nandakumar, G., Thomas, N., Lim, M., Qian, E., Jimeno, M.M., Poojar, P., Jin, Z., Quarterman, P., and Srinivasan, G. (2023). Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer\u2019s disease imaging. Front. Neuroimaging, 2.","DOI":"10.3389\/fnimg.2023.1072759"},{"key":"ref_257","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1111\/jon.12163","article-title":"Predictive Models Based on Support Vector Machines: Whole-Brain versus Regional Analysis of Structural MRI in the Alzheimer\u2019s Disease","volume":"25","author":"Retico","year":"2014","journal-title":"J. Neuroimaging"},{"key":"ref_258","doi-asserted-by":"crossref","unstructured":"Rodrigues, F., and Silveira, M. (2014). Longitudinal FDG-PET features for the classification of Alzheimer\u2019s disease. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE.","DOI":"10.1109\/EMBC.2014.6943992"},{"key":"ref_259","doi-asserted-by":"crossref","first-page":"119741","DOI":"10.1016\/j.neuroimage.2022.119741","article-title":"A benchmark for hypothalamus segmentation on T1-weighted MR images","volume":"264","author":"Rodrigues","year":"2022","journal-title":"NeuroImage"},{"key":"ref_260","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.neuroimage.2017.09.049","article-title":"HIPS: A new hippocampus subfield segmentation method","volume":"163","author":"Romero","year":"2017","journal-title":"NeuroImage"},{"key":"ref_261","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1109\/TMI.2014.2308999","article-title":"Extracting Salient Brain Patterns for Imaging-Based Classification of Neurodegenerative Diseases","volume":"33","author":"Rueda","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_262","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TMI.2012.2216543","article-title":"The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction","volume":"31","author":"Sabuncu","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_263","doi-asserted-by":"crossref","first-page":"120767","DOI":"10.1016\/j.neuroimage.2024.120767","article-title":"Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks","volume":"298","author":"Sackl","year":"2024","journal-title":"NeuroImage"},{"key":"ref_264","doi-asserted-by":"crossref","unstructured":"Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., Quattrone, A., and Castiglioni, I. (2015). Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer\u2019s disease: A machine learning approach. Front. Neurosci., 9.","DOI":"10.3389\/fnins.2015.00307"},{"key":"ref_265","doi-asserted-by":"crossref","first-page":"4091","DOI":"10.1002\/hbm.24687","article-title":"Accurate, rapid and reliable, fully automated MRI brainstem segmentation for application in multiple sclerosis and neurodegenerative diseases","volume":"40","author":"Sander","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"ref_266","doi-asserted-by":"crossref","unstructured":"Sarraf, S., DeSouza, D.D., Anderson, J., and Tofighi, G. (2016). DeepAD: Alzheimer\u2019s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI. bioRxiv.","DOI":"10.1101\/070441"},{"key":"ref_267","doi-asserted-by":"crossref","unstructured":"Savio, A., Garc\u00eda-Sebasti\u00e1n, M., Hern\u00e1ndez, C., Gra\u00f1a, M., and Villan\u00faa, J. (2009). Classification Results of Artificial Neural Networks for Alzheimer\u2019s Disease Detection. Intelligent Data Engineering and Automated Learning\u2014IDEAL 2009, Springer.","DOI":"10.1007\/978-3-642-04394-9_78"},{"key":"ref_268","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1016\/j.eswa.2012.09.009","article-title":"Deformation based feature selection for Computer Aided Diagnosis of Alzheimer\u2019s Disease","volume":"40","author":"Savio","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_269","doi-asserted-by":"crossref","unstructured":"Schmidt, M.T., Kanda, P.A.M., Basile, L.F.H., da Silva Lopes, H.F., Baratho, R., Demario, J.L.C., Jorge, M.S., Nardi, A.E., Machado, S., and Ianof, J.N. (2013). Index of Alpha\/Theta Ratio of the Electroencephalogram: A New Marker for Alzheimer\u2019s Disease. Front. Aging Neurosci., 5.","DOI":"10.3389\/fnagi.2013.00060"},{"key":"ref_270","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.compbiomed.2014.04.010","article-title":"A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer\u2019s disease and mild cognitive impairment","volume":"51","author":"Seixas","year":"2014","journal-title":"Comput. Biol. Med."},{"key":"ref_271","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s13139-022-00772-4","article-title":"MR Template-Based Individual Brain PET Volumes-of-Interest Generation Neither Using MR nor Using Spatial Normalization","volume":"57","author":"Seo","year":"2022","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_272","doi-asserted-by":"crossref","unstructured":"Seo, S.Y., Kim, S.J., Oh, J.S., Chung, J., Kim, S.Y., Oh, S.J., Joo, S., and Kim, J.S. (2022). Unified Deep Learning-Based Mouse Brain MR Segmentation: Template-Based Individual Brain Positron Emission Tomography Volumes-of-Interest Generation Without Spatial Normalization in Mouse Alzheimer Model. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.807903"},{"key":"ref_273","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.1002\/alz.12564","article-title":"Deep residual inception encoder-decoder network for amyloid PET harmonization","volume":"18","author":"Shah","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_274","doi-asserted-by":"crossref","unstructured":"Shah, J., Che, Y., Sohankar, J., Luo, J., Li, B., Su, Y., and Wu, T. (2024). Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models. Life, 14.","DOI":"10.20944\/preprints202411.0051.v1"},{"key":"ref_275","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1016\/j.neuroimage.2011.10.014","article-title":"Detecting global and local hippocampal shape changes in Alzheimer\u2019s disease using statistical shape models","volume":"59","author":"Shen","year":"2012","journal-title":"NeuroImage"},{"key":"ref_276","unstructured":"Shi, B., Wang, Z., and Liu, J. (2014). Distance-informed metric learning for Alzheimer\u2019s disease staging. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE."},{"key":"ref_277","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1002\/hbm.26146","article-title":"Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer\u2019s disease and mild cognitive impairment","volume":"44","author":"Shi","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_278","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.media.2014.01.001","article-title":"Quantifying anatomical shape variations in neurological disorders","volume":"18","author":"Singh","year":"2014","journal-title":"Med. Image Anal."},{"key":"ref_279","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1186\/gb-2013-14-7-125","article-title":"MicroRNAs circulate around Alzheimer\u2019s disease","volume":"14","author":"Slack","year":"2013","journal-title":"Genome Biol."},{"key":"ref_280","first-page":"1148","article-title":"Early detection of Alzheimer\u2019s disease using MRI hippocampal texture","volume":"37","author":"Igel","year":"2015","journal-title":"Hum. Brain Mapp."},{"key":"ref_281","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1111\/joim.12028","article-title":"An MRI-based index to measure the severity of Alzheimer\u2019s disease-like structural pattern in subjects with mild cognitive impairment","volume":"273","author":"Spulber","year":"2013","journal-title":"J. Intern. Med."},{"key":"ref_282","doi-asserted-by":"crossref","unstructured":"Suk, H.I., and Shen, D. (2013). Deep Learning-Based Feature Representation for AD\/MCI Classification. Advanced Information Systems Engineering, Springer.","DOI":"10.1007\/978-3-642-40763-5_72"},{"key":"ref_283","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s00429-013-0687-3","article-title":"Latent feature representation with stacked auto-encoder for AD\/MCI diagnosis","volume":"220","author":"Suk","year":"2013","journal-title":"Brain Struct. Funct."},{"key":"ref_284","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","article-title":"Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis","volume":"101","author":"Suk","year":"2014","journal-title":"NeuroImage"},{"key":"ref_285","doi-asserted-by":"crossref","unstructured":"Sun, Z., Veerman, J.A.C., and Jasinschi, R.S. (2012). A method for detecting interstructural atrophy correlation in MRI brain images. Proceedings of the 2012 19th IEEE International Conference on Image Processing, IEEE.","DOI":"10.1109\/ICIP.2012.6467094"},{"key":"ref_286","doi-asserted-by":"crossref","first-page":"599","DOI":"10.3233\/JAD-141605","article-title":"Baseline Shape Diffeomorphometry Patterns of Subcortical and Ventricular Structures in Predicting Conversion of Mild Cognitive Impairment to Alzheimer\u2019s Disease","volume":"44","author":"Tang","year":"2015","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_287","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1016\/S1474-4422(15)00093-9","article-title":"Multimodal imaging in Alzheimer\u2019s disease: Validity and usefulness for early detection","volume":"14","author":"Teipel","year":"2015","journal-title":"Lancet Neurol."},{"key":"ref_288","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11063-011-9200-2","article-title":"A Two Stage Sequential Ensemble Applied to the Classification of Alzheimer\u2019s Disease Based on MRI Features","volume":"35","author":"Termenon","year":"2011","journal-title":"Neural Process. Lett."},{"key":"ref_289","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.media.2014.04.006","article-title":"Multiple instance learning for classification of dementia in brain MRI","volume":"18","author":"Tong","year":"2014","journal-title":"Med. Image Anal."},{"key":"ref_290","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/s41514-025-00258-5","article-title":"Interpretable deep learning framework for understanding molecular changes in human brains with Alzheimer\u2019s disease: Implications for microglia activation and sex differences","volume":"11","author":"Trivedi","year":"2025","journal-title":"npj Aging"},{"key":"ref_291","doi-asserted-by":"crossref","unstructured":"Tward, D., Brown, T., Kageyama, Y., Patel, J., Hou, Z., Mori, S., Albert, M., Troncoso, J., and Miller, M. (2020). Diffeomorphic Registration with Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer\u2019s Disease. Front. Neurosci., 14.","DOI":"10.3389\/fnins.2020.00052"},{"key":"ref_292","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.neuroimage.2012.09.015","article-title":"Binary classification of 18F-flutemetamol PET using machine learning: Comparison with visual reads and structural MRI","volume":"64","author":"Vandenberghe","year":"2013","journal-title":"NeuroImage"},{"key":"ref_293","first-page":"9","article-title":"A New Approach for Alzheimer\u015b Disease Diagnosis by using Association Rule over PET Images","volume":"91","author":"Veeramuthu","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_294","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1016\/j.neuroimage.2007.09.073","article-title":"Alzheimer\u2019s disease diagnosis in individual subjects using structural MR images: Validation studies","volume":"39","author":"Vemuri","year":"2008","journal-title":"NeuroImage"},{"key":"ref_295","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1002\/hbm.20324","article-title":"Altered functional connectivity in early Alzheimer\u2019s disease: A resting-state fMRI study","volume":"28","author":"Wang","year":"2006","journal-title":"Hum. Brain Mapp."},{"key":"ref_296","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/TMI.2006.887380","article-title":"Large Deformation Diffeomorphism and Momentum Based Hippocampal Shape Discrimination in Dementia of the Alzheimer type","volume":"26","author":"Wang","year":"2007","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_297","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1002\/ima.22132","article-title":"Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection","volume":"25","author":"Wang","year":"2015","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_298","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3233\/JAD-150848","article-title":"Detection of Alzheimer\u2019s Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging","volume":"50","author":"Wang","year":"2015","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_299","doi-asserted-by":"crossref","unstructured":"Wang, H., Lei, C., Zhao, D., Gao, L., and Gao, J. (2023). DeepHipp: Accurate segmentation of hippocampus using 3D dense-block based on attention mechanism. BMC Med. Imaging, 23.","DOI":"10.1186\/s12880-023-01103-5"},{"key":"ref_300","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1016\/j.neuroimage.2010.10.026","article-title":"Enriched white matter connectivity networks for accurate identification of MCI patients","volume":"54","author":"Wee","year":"2011","journal-title":"NeuroImage"},{"key":"ref_301","doi-asserted-by":"crossref","first-page":"3411","DOI":"10.1002\/hbm.22156","article-title":"Prediction of Alzheimer\u2019s disease and mild cognitive impairment using cortical morphological patterns","volume":"34","author":"Wee","year":"2012","journal-title":"Hum. Brain Mapp."},{"key":"ref_302","doi-asserted-by":"crossref","unstructured":"Westman, E., Cavallin, L., Muehlboeck, J.S., Zhang, Y., Mecocci, P., Vellas, B., Tsolaki, M., K\u0142oszewska, I., Soininen, H., and Spenger, C. (2011). Sensitivity and Specificity of Medial Temporal Lobe Visual Ratings and Multivariate Regional MRI Classification in Alzheimer\u2019s Disease. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0022506"},{"key":"ref_303","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.neuroimage.2011.06.065","article-title":"AddNeuroMed and ADNI: Similar patterns of Alzheimer\u2019s atrophy and automated MRI classification accuracy in Europe and North America","volume":"58","author":"Westman","year":"2011","journal-title":"NeuroImage"},{"key":"ref_304","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1016\/j.neuroimage.2010.08.044","article-title":"Multivariate analysis of MRI data for Alzheimer\u2019s disease, mild cognitive impairment and healthy controls","volume":"54","author":"Westman","year":"2011","journal-title":"NeuroImage"},{"key":"ref_305","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s10548-012-0246-x","article-title":"Regional Magnetic Resonance Imaging Measures for Multivariate Analysis in Alzheimer\u2019s Disease and Mild Cognitive Impairment","volume":"26","author":"Westman","year":"2012","journal-title":"Brain Topogr."},{"key":"ref_306","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.neuroimage.2012.04.056","article-title":"Combining MRI and CSF measures for classification of Alzheimer\u2019s disease and prediction of mild cognitive impairment conversion","volume":"62","author":"Westman","year":"2012","journal-title":"NeuroImage"},{"key":"ref_307","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S0197-4580(00)00238-4","article-title":"Hippocampal volume discriminates between normal cognition; questionable and mild dementia in the elderly","volume":"22","author":"Wolf","year":"2001","journal-title":"Neurobiol. Aging"},{"key":"ref_308","doi-asserted-by":"crossref","unstructured":"Wolz, R., Julkunen, V., Koikkalainen, J., Niskanen, E., Zhang, D.P., Rueckert, D., Soininen, H., and L\u00f6tj\u00f6nen, J. (2011). Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer\u2019s Disease. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0025446"},{"key":"ref_309","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.media.2011.12.003","article-title":"Nonlinear dimensionality reduction combining MR imaging with non-imaging information","volume":"16","author":"Wolz","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_310","doi-asserted-by":"crossref","unstructured":"Wurts, A., Oakley, D.H., Hyman, B.T., and Samsi, S. (2020). Segmentation of Tau Stained Alzheimers Brain Tissue Using Convolutional Neural Networks. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC44109.2020.9175832"},{"key":"ref_311","doi-asserted-by":"crossref","first-page":"102169","DOI":"10.1016\/j.media.2021.102169","article-title":"Learning to synthesise the ageing brain without longitudinal data","volume":"73","author":"Xia","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_312","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.cmpb.2015.08.004","article-title":"Multi-modality sparse representation-based classification for Alzheimer\u2019s disease and mild cognitive impairment","volume":"122","author":"Xu","year":"2015","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_313","doi-asserted-by":"crossref","unstructured":"Yadav, S.K., Kafieh, R., Zimmermann, H.G., Kauer-Bonin, J., Nouri-Mahdavi, K., Mohammadzadeh, V., Shi, L., Kadas, E.M., Paul, F., and Motamedi, S. (2022). Intraretinal Layer Segmentation Using Cascaded Compressed U-Nets. J. Imaging, 8.","DOI":"10.3390\/jimaging8050139"},{"key":"ref_314","doi-asserted-by":"crossref","unstructured":"Yang, S.T., Lee, J.D., Huang, C.H., Wang, J.J., Hsu, W.C., and Wai, Y.Y. (2010). Computer-Aided Diagnosis of Alzheimer\u2019s Disease Using Multiple Features with Artificial Neural Network. PRICAI 2010: Trends in Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-642-15246-7_72"},{"key":"ref_315","doi-asserted-by":"crossref","first-page":"775","DOI":"10.3233\/JAD-2011-101371","article-title":"Independent Component Analysis-Based Classification of Alzheimer\u2019s Disease MRI Data","volume":"24","author":"Yang","year":"2011","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_316","doi-asserted-by":"crossref","first-page":"253670","DOI":"10.1155\/2013\/253670","article-title":"Discrimination between Alzheimer\u2019s Disease and Mild Cognitive Impairment Using SOM and PSO-SVM","volume":"2013","author":"Yang","year":"2013","journal-title":"Comput. Math. Methods Med."},{"key":"ref_317","doi-asserted-by":"crossref","first-page":"7065","DOI":"10.1038\/s41467-021-26703-z","article-title":"A deep learning framework identifies dimensional representations of Alzheimer\u2019s Disease from brain structure","volume":"12","author":"Yang","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_318","doi-asserted-by":"crossref","unstructured":"Ye, D.H., Pohl, K.M., and Davatzikos, C. (2011). Semi-supervised Pattern Classification: Application to Structural MRI of Alzheimer\u2019s Disease. Proceedings of the 2011 International Workshop on Pattern Recognition in NeuroImaging, IEEE.","DOI":"10.1109\/PRNI.2011.12"},{"key":"ref_319","doi-asserted-by":"crossref","unstructured":"Yoshida, N., Kageyama, H., Akai, H., Yasaka, K., Sugawara, H., Okada, Y., and Kunimatsu, A. (2022). Motion correction in MR image for analysis of VSRAD using generative adversarial network. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0274576"},{"key":"ref_320","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1002\/hbm.20632","article-title":"Patterns of structural complexity in Alzheimer\u2019s disease and frontotemporal dementia","volume":"30","author":"Young","year":"2008","journal-title":"Hum. Brain Mapp."},{"key":"ref_321","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.nicl.2013.05.004","article-title":"Accurate multimodal probabilistic prediction of conversion to Alzheimer\u2019s disease in patients with mild cognitive impairment","volume":"2","author":"Young","year":"2013","journal-title":"NeuroImage Clin."},{"key":"ref_322","doi-asserted-by":"crossref","unstructured":"Yu, G., Liu, Y., Thung, K.H., and Shen, D. (2014). Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0096458"},{"key":"ref_323","doi-asserted-by":"crossref","unstructured":"Yun, H.J., Kwak, K., and Lee, J.M. (2015). Multimodal Discrimination of Alzheimer\u2019s Disease Based on Regional Cortical Atrophy and Hypometabolism. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0129250"},{"key":"ref_324","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1161\/STROKEAHA.108.530832","article-title":"Regional White Matter Integrity Differentiates Between Vascular Dementia and Alzheimer Disease","volume":"40","author":"Zarei","year":"2009","journal-title":"Stroke"},{"key":"ref_325","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1109\/TMI.2011.2114362","article-title":"ODVBA: Optimally-Discriminative Voxel-Based Analysis","volume":"30","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_326","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.neuroimage.2011.09.069","article-title":"Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer\u2019s disease","volume":"59","author":"Zhang","year":"2012","journal-title":"NeuroImage"},{"key":"ref_327","doi-asserted-by":"crossref","unstructured":"Zhang, D., and Shen, D. (2012). Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0033182"},{"key":"ref_328","doi-asserted-by":"crossref","first-page":"171","DOI":"10.2528\/PIER13121310","article-title":"Classification of Alzheimer Disease based on structural magnetic resonance imaging by kernel support vector machine decision tree","volume":"144","author":"Zhang","year":"2014","journal-title":"Prog. Electromagn. Res."},{"key":"ref_329","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Huang, H., and Shen, D. (2014). Integrative analysis of multi-dimensional imaging genomics data for Alzheimer\u2019s disease prediction. Front. Aging Neurosci., 6.","DOI":"10.3389\/fnagi.2014.00260"},{"key":"ref_330","doi-asserted-by":"crossref","first-page":"e1251","DOI":"10.7717\/peerj.1251","article-title":"Detection of Alzheimer\u2019s disease by displacement field and machine learning","volume":"3","author":"Zhang","year":"2015","journal-title":"PeerJ"},{"key":"ref_331","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Dong, Z., Phillips, P., Wang, S., Ji, G., Yang, J., and Yuan, T.F. (2015). Detection of subjects and brain regions related to Alzheimer\u2019s disease using 3D MRI scans based on eigenbrain and machine learning. Front. Comput. Neurosci., 9.","DOI":"10.3389\/fncom.2015.00066"},{"key":"ref_332","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.1002\/jmri.27984","article-title":"Improving Sensitivity of Arterial Spin Labeling Perfusion MRI in Alzheimer\u2019s Disease Using Transfer Learning of Deep Learning-Based ASL Denoising","volume":"55","author":"Zhang","year":"2021","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_333","doi-asserted-by":"crossref","first-page":"103165","DOI":"10.1016\/j.media.2024.103165","article-title":"Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning","volume":"95","author":"Zhang","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_334","doi-asserted-by":"crossref","first-page":"e0587242024","DOI":"10.1523\/JNEUROSCI.0587-24.2024","article-title":"G-Protein Signaling in Alzheimer\u2019s Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework","volume":"44","author":"Zhang","year":"2024","journal-title":"J. Neurosci."},{"key":"ref_335","doi-asserted-by":"crossref","first-page":"995","DOI":"10.3233\/JAD-150311","article-title":"Novel Cortical Thickness Pattern for Accurate Detection of Alzheimer\u2019s Disease","volume":"48","author":"Zheng","year":"2015","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_336","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1093\/brain\/awq075","article-title":"Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer\u2019s disease","volume":"133","author":"Zhou","year":"2010","journal-title":"Brain"},{"key":"ref_337","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1109\/TBME.2014.2310709","article-title":"An Optimal Decisional Space for the Classification of Alzheimer\u2019s Disease and Mild Cognitive Impairment","volume":"61","author":"Zhou","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_338","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.neuroimage.2014.05.078","article-title":"A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis","volume":"100","author":"Zhu","year":"2014","journal-title":"NeuroImage"},{"key":"ref_339","doi-asserted-by":"crossref","unstructured":"Zhu, X., Suk, H.I., Zhu, Y., Thung, K.H., Wu, G., and Shen, D. (2015). Multi-view Classification for Identification of Alzheimer\u2019s Disease. Machine Learning in Medical Imaging, Springer International Publishing.","DOI":"10.1007\/978-3-319-24888-2_31"},{"key":"ref_340","doi-asserted-by":"crossref","first-page":"103863","DOI":"10.1016\/j.jbi.2021.103863","article-title":"Alzheimer\u2019s disease diagnosis framework from incomplete multimodal data using convolutional neural networks","volume":"121","author":"Abdelaziz","year":"2021","journal-title":"J. Biomed. Inform."},{"key":"ref_341","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1007\/s10916-019-1428-9","article-title":"Automated Detection of Alzheimer\u2019s Disease Using Brain MRI Images\u2014A Study with Various Feature Extraction Techniques","volume":"43","author":"Acharya","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_342","doi-asserted-by":"crossref","first-page":"108652","DOI":"10.1016\/j.cmpb.2025.108652","article-title":"EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer\u2019s Disease and Frontotemporal Dementia using EEG signals","volume":"262","author":"Acharya","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_343","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1109\/TNSRE.2024.3374651","article-title":"EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset","volume":"32","author":"Adebisi","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_344","doi-asserted-by":"crossref","unstructured":"Aderghal, K., Boissenin, M., Benois-Pineau, J., Catheline, G., and Afdel, K. (2016). Classification of sMRI for AD Diagnosis with Convolutional Neuronal Networks: A Pilot 2-D+ epsilon Study on ADNI. MultiMedia Modeling, Springer International Publishing.","DOI":"10.1007\/978-3-319-51811-4_56"},{"key":"ref_345","doi-asserted-by":"crossref","unstructured":"Aderghal, K., Benois-Pineau, J., Afdel, K., and Gwena\u00eblle, C. (2017). FuseMe: Classification of sMRI images by fusion of Deep CNNs in 2D+\u03f5 projections. Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing, ACM. CBMI \u201917.","DOI":"10.1145\/3095713.3095749"},{"key":"ref_346","doi-asserted-by":"crossref","unstructured":"Aderghal, K., Khvostikov, A., Krylov, A., Benois-Pineau, J., Afdel, K., and Catheline, G. (2018). Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning. Proceedings of the 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), IEEE.","DOI":"10.1109\/CBMS.2018.00067"},{"key":"ref_347","doi-asserted-by":"crossref","unstructured":"Ahmad, A.L., Idris, Z., Faye, I., Sanchez-Bornot, J., Sotero, R.C., and Coyle, D. (2024). Magnetoencephalography Brain Spectral Biomarkers of Alzheimer\u2019s Disease: A Promising Approach Demonstrated with the BioFIND Dataset. Proceedings of the 2024 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), IEEE.","DOI":"10.1109\/IECBES61011.2024.10991297"},{"key":"ref_348","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.neucom.2016.08.041","article-title":"Recognition of Alzheimer\u2019s disease and Mild Cognitive Impairment with multimodal image-derived biomarkers and Multiple Kernel Learning","volume":"220","author":"Ahmed","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_349","doi-asserted-by":"crossref","first-page":"51","DOI":"10.19101\/IJACR.2021.1152001","article-title":"Multi-classification of alzheimer disease on magnetic resonance images (MRI) using deep convolutional neural network (DCNN) approaches","volume":"11","author":"Ajagbe","year":"2021","journal-title":"Int. J. Adv. Comput. Res."},{"key":"ref_350","doi-asserted-by":"crossref","first-page":"43742","DOI":"10.1038\/s41598-025-28070-x","article-title":"NeuroFusionNet: A hybrid EEG feature fusion framework for accurate and explainable Alzheimer\u2019s Disease detection","volume":"15","author":"Akbar","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_351","doi-asserted-by":"crossref","unstructured":"Aland, B., Chandana, T., Bachanna, P., and Pradeep, M.B. (2025). AlzMind: Integrating MRI and EEG for AI-Powered Alzheimer\u2019s Prediction. Proceedings of the 2025 IEEE 7th International Conference on Computing, Communication and Automation (ICCCA), IEEE.","DOI":"10.1109\/ICCCA66364.2025.11325379"},{"key":"ref_352","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.1038\/s41598-024-51985-w","article-title":"An explainable machine learning approach for Alzheimer\u2019s disease classification","volume":"14","author":"Alatrany","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_353","doi-asserted-by":"crossref","unstructured":"Alessandrini, M., Biagetti, G., Crippa, P., Falaschetti, L., Luzzi, S., and Turchetti, C. (2022). EEG-Based Alzheimer\u2019s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network. Sensors, 22.","DOI":"10.3390\/s22103696"},{"key":"ref_354","unstructured":"Alickovic, E., and Subasi, A. (2019). Automatic Detection of Alzheimer Disease Based on Histogram and Random Forest. CMBEBIH 2019, Springer International Publishing."},{"key":"ref_355","doi-asserted-by":"crossref","first-page":"105462","DOI":"10.1016\/j.bspc.2023.105462","article-title":"Mild cognitive impairment detection with optimally selected EEG channels based on variational mode decomposition and supervised machine learning","volume":"87","author":"Aljalal","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_356","doi-asserted-by":"crossref","unstructured":"Aljovic, A., Badnjevic, A., and Gurbeta, L. (2016). Artificial neural networks in the discrimination of Alzheimer\u2019s disease using biomarkers data. Proceedings of the 2016 5th Mediterranean Conference on Embedded Computing (MECO), IEEE.","DOI":"10.1109\/MECO.2016.7525762"},{"key":"ref_357","doi-asserted-by":"crossref","first-page":"106240","DOI":"10.1016\/j.compbiomed.2022.106240","article-title":"Multi-label classification of Alzheimer\u2019s disease stages from resting-state fMRI-based correlation connectivity data and deep learning","volume":"151","author":"Alorf","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_358","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.bspc.2018.02.019","article-title":"Multi-class Alzheimer\u2019s disease classification using image and clinical features","volume":"43","author":"Altaf","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_359","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.jneumeth.2019.04.013","article-title":"A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer\u2019s disease using EEG signals","volume":"322","author":"Mammone","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_360","doi-asserted-by":"crossref","first-page":"5511922","DOI":"10.1155\/2021\/5511922","article-title":"Diagnosis of Alzheimer\u2019s Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal","volume":"2021","author":"Amini","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_361","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, T., Teixeira, J.P., and Rodrigues, P.M. (2022). Smart-Data-Driven System for Alzheimer Disease Detection through Electroencephalographic Signals. Bioengineering, 9.","DOI":"10.3390\/bioengineering9040141"},{"key":"ref_362","doi-asserted-by":"crossref","unstructured":"Arpaia, P., Cacciapuoti, M., Cataldo, A., Criscuolo, S., De Benedetto, E., Masciullo, A., Pesola, M., and Schiavoni, R. (2025). Assessing the Role of EEG Biosignal Preprocessing to Enhance Multiscale Fuzzy Entropy in Alzheimer\u2019s Disease Detection. Biosensors, 15.","DOI":"10.3390\/bios15060374"},{"key":"ref_363","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.trci.2017.01.006","article-title":"Predicting mild cognitive impairment from spontaneous spoken utterances","volume":"3","author":"Asgari","year":"2017","journal-title":"Alzheimer\u2019s Dement. Transl. Res. Clin. Interv."},{"key":"ref_364","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.bspc.2018.10.010","article-title":"A hybrid feature extraction approach for brain MRI classification based on Bag-of-words","volume":"48","author":"Ayadi","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_365","doi-asserted-by":"crossref","first-page":"247","DOI":"10.3233\/JAD-170703","article-title":"Abnormalities of Resting State Cortical EEG Rhythms in Subjects with Mild Cognitive Impairment Due to Alzheimer\u2019s and Lewy Body Diseases","volume":"62","author":"Babiloni","year":"2018","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_366","doi-asserted-by":"crossref","unstructured":"Backstrom, K., Nazari, M., Gu, I.Y.H., and Jakola, A.S. (2018). An efficient 3D deep convolutional network for Alzheimer\u2019s disease diagnosis using MR images. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE.","DOI":"10.1109\/ISBI.2018.8363543"},{"key":"ref_367","first-page":"403","article-title":"EEG signal analysis for early diagnosis of Alzheimer disease using spectral and wavelet based features","volume":"10","author":"Bairagi","year":"2018","journal-title":"Int. J. Inf. Technol."},{"key":"ref_368","doi-asserted-by":"crossref","first-page":"109810","DOI":"10.1016\/j.compbiomed.2025.109810","article-title":"Alzheimer\u2019s Disease detection and classification using optimized neural network","volume":"187","author":"Balakrishnan","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_369","doi-asserted-by":"crossref","first-page":"101645","DOI":"10.1016\/j.nicl.2018.101645","article-title":"Automated classification of Alzheimer\u2019s disease and mild cognitive impairment using a single MRI and deep neural networks","volume":"21","author":"Basaia","year":"2019","journal-title":"NeuroImage Clin."},{"key":"ref_370","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1016\/j.trci.2019.10.001","article-title":"Convolution neural network\u2013based Alzheimer\u2019s disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation","volume":"5","author":"Basheera","year":"2019","journal-title":"Alzheimer\u2019s Dement. Transl. Res. Clin. Interv."},{"key":"ref_371","doi-asserted-by":"crossref","unstructured":"Battineni, G., Chintalapudi, N., and Amenta, F. (2020). Comparative Machine Learning Approach in Dementia Patient Classification using Principal Component Analysis. Proceedings of the 12th International Conference on Agents and Artificial Intelligence, SCITEPRESS\u2014Science and Technology Publications.","DOI":"10.5220\/0009096907800784"},{"key":"ref_372","doi-asserted-by":"crossref","unstructured":"Battineni, G., Hossain, M.A., Chintalapudi, N., Traini, E., Dhulipalla, V.R., Ramasamy, M., and Amenta, F. (2021). Improved Alzheimer\u2019s Disease Detection by MRI Using Multimodal Machine Learning Algorithms. Diagnostics, 11.","DOI":"10.3390\/diagnostics11112103"},{"key":"ref_373","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.3233\/JAD-160850","article-title":"Histogram-Based Feature Extraction from Individual Gray Matter Similarity-Matrix for Alzheimer\u2019s Disease Classification","volume":"55","author":"Beheshti","year":"2016","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_374","doi-asserted-by":"crossref","first-page":"295","DOI":"10.3233\/JAD-161080","article-title":"Classification of Alzheimer\u2019s Disease and Prediction of Mild Cognitive Impairment Conversion Using Histogram-Based Analysis of Patient-Specific Anatomical Brain Connectivity Networks","volume":"60","author":"Beheshti","year":"2017","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_375","doi-asserted-by":"crossref","unstructured":"Bi, X., Jiang, Q., Sun, Q., Shu, Q., and Liu, Y. (2018). Analysis of Alzheimer\u2019s Disease Based on the Random Neural Network Cluster in fMRI. Front. Neuroinform., 12.","DOI":"10.3389\/fninf.2018.00060"},{"key":"ref_376","doi-asserted-by":"crossref","unstructured":"Binzagr, F., and Abulfaraj, A.W. (2025). InGSA: Integrating generalized self-attention in CNN for Alzheimer\u2019s disease classification. Front. Artif. Intell., 8.","DOI":"10.3389\/frai.2025.1540646"},{"key":"ref_377","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1186\/s13195-021-00879-4","article-title":"Data analysis with Shapley values for automatic subject selection in Alzheimer\u2019s disease data sets using interpretable machine learning","volume":"13","author":"Bloch","year":"2021","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_378","doi-asserted-by":"crossref","unstructured":"B\u00f6hle, M., Eitel, F., Weygandt, M., and Ritter, K. (2019). Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer\u2019s Disease Classification. Front. Aging Neurosci., 11.","DOI":"10.3389\/fnagi.2019.00194"},{"key":"ref_379","doi-asserted-by":"crossref","unstructured":"Carbonaro, M., Zaccardi, S., Seoni, S., Meiburger, K.M., and Botter, A. (2022). Detecting anatomical characteristics of single motor units by combining high density electromyography and ultrafast ultrasound: A simulation study. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC48229.2022.9871578"},{"key":"ref_380","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1007\/s11517-021-02427-6","article-title":"Novelty detection-based approach for Alzheimer\u2019s disease and mild cognitive impairment diagnosis from EEG","volume":"59","author":"Cejnek","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_381","doi-asserted-by":"crossref","unstructured":"Chatterjee, S., and Byun, Y.C. (2022). Voting Ensemble Approach for Enhancing Alzheimer\u2019s Disease Classification. Sensors, 22.","DOI":"10.3390\/s22197661"},{"key":"ref_382","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.pscychresns.2017.04.004","article-title":"Automated detection of pathologic white matter alterations in Alzheimer\u2019s disease using combined diffusivity and kurtosis method","volume":"264","author":"Chen","year":"2017","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"ref_383","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s12021-016-9318-5","article-title":"Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer\u2019s Disease","volume":"15","author":"Cheng","year":"2016","journal-title":"Neuroinformatics"},{"key":"ref_384","doi-asserted-by":"crossref","unstructured":"Cheng, D., and Liu, M. (2017). CNNs based multi-modality classification for AD diagnosis. Proceedings of the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE.","DOI":"10.1109\/CISP-BMEI.2017.8302281"},{"key":"ref_385","doi-asserted-by":"crossref","first-page":"1042042","DOI":"10.1117\/12.2281808","article-title":"Classification of MR brain images by combination of multi-CNNs for AD diagnosis","volume":"Volume 10420","author":"Falco","year":"2017","journal-title":"Proceedings of the Ninth International Conference on Digital Image Processing (ICDIP 2017)"},{"key":"ref_386","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1007\/s11682-018-9846-8","article-title":"Robust multi-label transfer feature learning for early diagnosis of Alzheimer\u2019s disease","volume":"13","author":"Cheng","year":"2018","journal-title":"Brain Imaging Behav."},{"key":"ref_387","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.bbr.2018.02.017","article-title":"Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging","volume":"344","author":"Choi","year":"2018","journal-title":"Behav. Brain Res."},{"key":"ref_388","first-page":"113","article-title":"Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment","volume":"2","author":"Clark","year":"2016","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_389","doi-asserted-by":"crossref","unstructured":"Cui, R., Liu, M., and Li, G. (2018). Longitudinal analysis for Alzheimer\u2019s disease diagnosis using RNN. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE.","DOI":"10.1109\/ISBI.2018.8363833"},{"key":"ref_390","doi-asserted-by":"crossref","first-page":"e99010042","DOI":"10.14440\/jbm.2025.0069","article-title":"Electroencephalogram-based time-frequency analysis for Alzheimer\u2019s disease detection using machine learning","volume":"12","author":"Rodrigues","year":"2024","journal-title":"J. Biol. Methods"},{"key":"ref_391","doi-asserted-by":"crossref","first-page":"771","DOI":"10.3233\/ADR-210314","article-title":"Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer\u2019s Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques","volume":"5","author":"Das","year":"2021","journal-title":"J. Alzheimer\u2019s Dis. Rep."},{"key":"ref_392","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10278-022-00719-3","article-title":"An Explainable Convolutional Neural Network for the Early Diagnosis of Alzheimer\u2019s Disease from 18F-FDG PET","volume":"36","author":"Pasini","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_393","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1002\/alz.13411","article-title":"Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG","volume":"20","author":"Deng","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_394","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1007\/s12559-025-10528-9","article-title":"Diagnosis of Alzheimer\u2019s Disease Via Machine Learning Approaches with Integrated Resting-State EEG and ERP Characteristics","volume":"17","author":"Deng","year":"2025","journal-title":"Cogn. Comput."},{"key":"ref_395","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.21037\/qims-21-430","article-title":"Fully automated discrimination of Alzheimer\u2019s disease using resting-state electroencephalography signals","volume":"12","author":"Ding","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_396","doi-asserted-by":"crossref","first-page":"106116","DOI":"10.1016\/j.compbiomed.2022.106116","article-title":"Latent feature representation learning for Alzheimer\u2019s disease classification","volume":"150","author":"Dong","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_397","doi-asserted-by":"crossref","first-page":"3822","DOI":"10.1038\/s41598-017-04204-8","article-title":"Towards affordable biomarkers of frontotemporal dementia: A classification study via network\u2019s information sharing","volume":"7","author":"Dottori","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_398","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MMUL.2022.3156471","article-title":"Multimodal Fusion-Based Deep Learning Network for Effective Diagnosis of Alzheimer\u2019s Disease","volume":"29","author":"Dwivedi","year":"2022","journal-title":"IEEE Multimed."},{"key":"ref_399","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.aej.2022.07.062","article-title":"An MRI-based deep learning approach for accurate detection of Alzheimer\u2019s disease","volume":"63","author":"Moustafa","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_400","doi-asserted-by":"crossref","first-page":"3463","DOI":"10.1038\/s41598-024-53733-6","article-title":"A novel CNN architecture for accurate early detection and classification of Alzheimer\u2019s disease using MRI data","volume":"14","author":"Amer","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_401","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1038\/s41598-021-82098-3","article-title":"A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer\u2019s disease","volume":"11","author":"Alonso","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_402","doi-asserted-by":"crossref","first-page":"22381","DOI":"10.1038\/s41598-022-26958-6","article-title":"A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm","volume":"12","author":"Elgammal","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_403","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.3233\/JAD-190262","article-title":"Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer\u2019s Disease","volume":"71","author":"Ezzati","year":"2019","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_404","doi-asserted-by":"crossref","unstructured":"Fan, M., Yang, A.C., Fuh, J.L., and Chou, C.A. (2018). Topological Pattern Recognition of Severe Alzheimer\u2019s Disease via Regularized Supervised Learning of EEG Complexity. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00685"},{"key":"ref_405","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1049\/iet-ipr.2019.0617","article-title":"Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer\u2019s disease diagnosis","volume":"14","author":"Fang","year":"2020","journal-title":"IET Image Process."},{"key":"ref_406","doi-asserted-by":"crossref","first-page":"116795","DOI":"10.1016\/j.neuroimage.2020.116795","article-title":"A comparison of resting state EEG and structural MRI for classifying Alzheimer\u2019s disease and mild cognitive impairment","volume":"215","author":"Farina","year":"2020","journal-title":"NeuroImage"},{"key":"ref_407","doi-asserted-by":"crossref","unstructured":"Feng, C., Elazab, A., Yang, P., Wang, T., Lei, B., and Xiao, X. (2018). 3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer\u2019s Disease Diagnosis. Predictive Intelligence in MEdicine, Springer International Publishing.","DOI":"10.1007\/978-3-030-00320-3_17"},{"key":"ref_408","doi-asserted-by":"crossref","first-page":"63605","DOI":"10.1109\/ACCESS.2019.2913847","article-title":"Deep Learning Framework for Alzheimer\u2019s Disease Diagnosis via 3D-CNN and FSBi-LSTM","volume":"7","author":"Feng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_409","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1590\/1516-4446-2016-2083","article-title":"Support vector machine-based classification of neuroimages in Alzheimer\u2019s disease: Direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals","volume":"40","author":"Ferreira","year":"2017","journal-title":"Rev. Bras. Psiquiatr."},{"key":"ref_410","doi-asserted-by":"crossref","unstructured":"Forouzannezhad, P., Abbaspour, A., Li, C., Cabrerizo, M., and Adjouadi, M. (2018). A Deep Neural Network Approach for Early Diagnosis of Mild Cognitive Impairment Using Multiple Features. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE.","DOI":"10.1109\/ICMLA.2018.00218"},{"key":"ref_411","doi-asserted-by":"crossref","unstructured":"Fritsch, J., Wankerl, S., and Noth, E. (2019). Automatic Diagnosis of Alzheimer\u2019s Disease Using Neural Network Language Models. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.","DOI":"10.1109\/ICASSP.2019.8682690"},{"key":"ref_412","doi-asserted-by":"crossref","first-page":"5693","DOI":"10.1038\/s41598-025-85802-9","article-title":"Dense convolution-based attention network for Alzheimer\u2019s disease classification","volume":"15","author":"Gan","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_413","doi-asserted-by":"crossref","unstructured":"Ganesan, P., Ramesh, G.P., Falkowski-Gilski, P., and Falkowska-Gilska, B. (2024). Detection of Alzheimer\u2019s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network. Front. Physiol., 15.","DOI":"10.3389\/fphys.2024.1380459"},{"key":"ref_414","doi-asserted-by":"crossref","first-page":"2737","DOI":"10.1007\/s11517-022-02630-z","article-title":"GA-MADRID: Design and validation of a machine learning tool for the diagnosis of Alzheimer\u2019s disease and frontotemporal dementia using genetic algorithms","volume":"60","author":"Pytel","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_415","doi-asserted-by":"crossref","first-page":"e200184","DOI":"10.1148\/ryai.2021200184","article-title":"A Deep Learning\u2013based Model for Detecting Abnormalities on Brain MR Images for Triaging: Preliminary Results from a Multisite Experience","volume":"3","author":"Gauriau","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"ref_416","doi-asserted-by":"crossref","first-page":"584","DOI":"10.2741\/4606","article-title":"Alzheimer\u2019s disease diagnostics by a 3D deeply supervised adaptable convolutional network","volume":"23","author":"Ghazal","year":"2018","journal-title":"Front. Biosci."},{"key":"ref_417","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.1016\/j.procs.2024.04.258","article-title":"Machine learning and texture features based approach for classifying Alzheimer\u2019s disease","volume":"235","author":"Gill","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_418","doi-asserted-by":"crossref","first-page":"14651","DOI":"10.1007\/s00521-021-06105-4","article-title":"Deep-MEG: Spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer\u2019s disease with magnetoencephalography","volume":"33","author":"Giovannetti","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_419","first-page":"187","article-title":"A Long Short-Term Memory Deep Learning Network for MRI Based Alzheimer\u2019s Disease Dementia Classification","volume":"9","author":"Gnanasegar","year":"2020","journal-title":"J. Appl. Bioinform. Comput. Biol."},{"key":"ref_420","doi-asserted-by":"crossref","unstructured":"Goerttler, S., He, F., and Wu, M. (2024). Balancing Spectral, Temporal and Spatial Information for EEG-based Alzheimer\u2019s Disease Classification. Proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC53108.2024.10782936"},{"key":"ref_421","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1093\/jamia\/ocac168","article-title":"Multimodal attention-based deep learning for Alzheimer\u2019s disease diagnosis","volume":"29","author":"Golovanevsky","year":"2022","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_422","doi-asserted-by":"crossref","unstructured":"Gopu, G., Sathish, S., Shanmathan, T.S., Suthakar, G., Sivakumaran, N., and Geetha Devasena, M.S. (2024). Multi Modal EEG-Based Classification of Alzheimer\u2019s Disease and Mild Cognitive Impairment Using Hilbert Huang Transform. Proceedings of the 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), IEEE.","DOI":"10.1109\/AMATHE61652.2024.10582221"},{"key":"ref_423","doi-asserted-by":"crossref","unstructured":"Gunawardena, K.A.N.N.P., Rajapakse, R.N., and Kodikara, N.D. (2017). Applying convolutional neural networks for pre-detection of alzheimer\u2019s disease from structural MRI data. Proceedings of the 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), IEEE.","DOI":"10.1109\/M2VIP.2017.8211486"},{"key":"ref_424","doi-asserted-by":"crossref","unstructured":"Gupta, Y., Lama, R.K., and Kwon, G.R. (2019). Prediction and Classification of Alzheimer\u2019s Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers. Front. Comput. Neurosci., 13.","DOI":"10.3389\/fncom.2019.00072"},{"key":"ref_425","doi-asserted-by":"crossref","unstructured":"Gupta, Y., Kim, J.I., Kim, B.C., and Kwon, G.R. (2020). Classification and Graphical Analysis of Alzheimer\u2019s Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype. Front. Aging Neurosci., 12.","DOI":"10.3389\/fnagi.2020.00238"},{"key":"ref_426","doi-asserted-by":"crossref","first-page":"110377","DOI":"10.1016\/j.jneumeth.2025.110377","article-title":"Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer\u2019s disease and frontotemporal dementia","volume":"416","author":"Hachamnia","year":"2025","journal-title":"J. Neurosci. Methods"},{"key":"ref_427","doi-asserted-by":"crossref","first-page":"101625","DOI":"10.1016\/j.media.2019.101625","article-title":"Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer\u2019s disease","volume":"60","author":"Hao","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_428","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1159\/000538486","article-title":"Feature Importance Analysis and Machine Learning for Alzheimer\u2019s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio","volume":"8","author":"Hassouneh","year":"2024","journal-title":"Digit. Biomark."},{"key":"ref_429","doi-asserted-by":"crossref","first-page":"8576","DOI":"10.1016\/j.jksuci.2021.09.003","article-title":"An experimental analysis of different Deep Learning based Models for Alzheimer\u2019s Disease classification using Brain Magnetic Resonance Images","volume":"34","author":"Hazarika","year":"2022","journal-title":"J. King Saud Univ.\u2014Comput. Inf. Sci."},{"key":"ref_430","doi-asserted-by":"crossref","first-page":"5509","DOI":"10.1038\/s41598-026-35184-3","article-title":"AI-driven framework for accurate detection of Alzheimer\u2019s disease in EEG","volume":"16","author":"Hemalatha","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_431","first-page":"260","article-title":"Computer-based evaluation of Alzheimer\u2019s disease and mild cognitive impairment patients during a picture description task","volume":"10","year":"2018","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_432","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compmedimag.2018.08.002","article-title":"Adaptive fusion of texture-based grading for Alzheimer\u2019s disease classification","volume":"70","author":"Hett","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_433","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jneumeth.2017.03.006","article-title":"Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM","volume":"282","author":"Hojjati","year":"2017","journal-title":"J. Neurosci. Methods"},{"key":"ref_434","doi-asserted-by":"crossref","unstructured":"Hon, M., and Khan, N.M. (2017). Towards Alzheimer\u2019s disease classification through transfer learning. Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.","DOI":"10.1109\/BIBM.2017.8217822"},{"key":"ref_435","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.media.2016.07.012","article-title":"Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features","volume":"34","author":"Hor","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_436","doi-asserted-by":"crossref","unstructured":"Hosseini-Asl, E., Keynton, R., and El-Baz, A. (2016). Alzheimer\u2019s disease diagnostics by adaptation of 3D convolutional network. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP.2016.7532332"},{"key":"ref_437","doi-asserted-by":"crossref","unstructured":"Houmani, N., Vialatte, F., Gallego-Jutgl\u00e0, E., Dreyfus, G., Nguyen-Michel, V.H., Mariani, J., and Kinugawa, K. (2018). Diagnosis of Alzheimer\u2019s disease with Electroencephalography in a differential framework. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0193607"},{"key":"ref_438","doi-asserted-by":"crossref","unstructured":"Huang, Y., Xu, J., Zhou, Y., Tong, T., and Zhuang, X. (2019). Diagnosis of Alzheimer\u2019s Disease via Multi-Modality 3D Convolutional Neural Network. Front. Neurosci., 13.","DOI":"10.3389\/fnins.2019.00509"},{"key":"ref_439","doi-asserted-by":"crossref","first-page":"5551","DOI":"10.1109\/JBHI.2024.3412812","article-title":"Ensemble Vision Transformer for Dementia Diagnosis","volume":"28","author":"Huang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_440","doi-asserted-by":"crossref","first-page":"11616","DOI":"10.1038\/s41598-025-86635-2","article-title":"A fine-tuned convolutional neural network model for accurate Alzheimer\u2019s disease classification","volume":"15","author":"Hussain","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_441","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.neucom.2018.09.071","article-title":"A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings","volume":"323","author":"Ieracitano","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_442","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.neunet.2019.12.006","article-title":"A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia","volume":"123","author":"Ieracitano","year":"2020","journal-title":"Neural Netw."},{"key":"ref_443","doi-asserted-by":"crossref","first-page":"9132","DOI":"10.1038\/s41598-020-66034-5","article-title":"Spontaneous MEG activity of the cerebral cortex during eyes closed and open discriminates Alzheimer\u2019s disease from cognitively normal older adults","volume":"10","author":"Ikeda","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_444","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40708-018-0080-3","article-title":"Brain MRI analysis for Alzheimer\u2019s disease diagnosis using an ensemble system of deep convolutional neural networks","volume":"5","author":"Islam","year":"2018","journal-title":"Brain Inform."},{"key":"ref_445","doi-asserted-by":"crossref","first-page":"154324","DOI":"10.1109\/ACCESS.2021.3127394","article-title":"A Novel AI-Based System for Detection and Severity Prediction of Dementia Using MRI","volume":"9","author":"Jain","year":"2021","journal-title":"IEEE Access"},{"key":"ref_446","doi-asserted-by":"crossref","unstructured":"Jang, H., Kim, S.K., Ha, J., and Kim, L. (2024). Early Detection of Alzheimer\u2019s Disease through Analysis of EEG Responses to Word Recognition. Proceedings of the 2024 12th International Winter Conference on Brain-Computer Interface (BCI), IEEE.","DOI":"10.1109\/BCI60775.2024.10480495"},{"key":"ref_447","doi-asserted-by":"crossref","first-page":"e0688242024","DOI":"10.1523\/JNEUROSCI.0688-24.2024","article-title":"A Shift Toward Supercritical Brain Dynamics Predicts Alzheimer\u2019s Disease Progression","volume":"45","author":"Javed","year":"2024","journal-title":"J. Neurosci."},{"key":"ref_448","doi-asserted-by":"crossref","first-page":"9060124","DOI":"10.1155\/2017\/9060124","article-title":"Diagnosis of Alzheimer\u2019s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network","volume":"2017","author":"Jha","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"ref_449","doi-asserted-by":"crossref","unstructured":"Ji, H., Liu, Z., Yan, W.Q., and Klette, R. (2019). Early Diagnosis of Alzheimer\u2019s Disease Using Deep Learning. Proceedings of the 2nd International Conference on Control and Computer Vision, ACM. ICCCV 2019.","DOI":"10.1145\/3341016.3341024"},{"key":"ref_450","doi-asserted-by":"crossref","first-page":"19585","DOI":"10.1007\/s00521-022-07501-0","article-title":"Deep learning and multimodal feature fusion for the aided diagnosis of Alzheimer\u2019s disease","volume":"34","author":"Jia","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_451","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13195-023-01181-1","article-title":"Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer\u2019s disease using EEG technology","volume":"15","author":"Jiao","year":"2023","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_452","doi-asserted-by":"crossref","unstructured":"Jin, L., Zhao, K., Zhao, Y., Che, T., and Li, S. (2022). A Hybrid Deep Learning Method for Early and Late Mild Cognitive Impairment Diagnosis with Incomplete Multimodal Data. Front. Neuroinform., 16.","DOI":"10.3389\/fninf.2022.843566"},{"key":"ref_453","doi-asserted-by":"crossref","first-page":"4284","DOI":"10.1038\/s41598-022-08231-y","article-title":"Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data","volume":"12","author":"Jitsuishi","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_454","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3107056","article-title":"Alzheimer\u2019s Patient Analysis Using Image and Gene Expression Data and Explainable-AI to Present Associated Genes","volume":"70","author":"Kamal","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_455","doi-asserted-by":"crossref","unstructured":"Kang, L., Jiang, J., Huang, J., and Zhang, T. (2020). Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning. Front. Aging Neurosci., 12.","DOI":"10.3389\/fnagi.2020.00206"},{"key":"ref_456","doi-asserted-by":"crossref","unstructured":"Kang, J., Kang, H., and Seo, J.H. (2025). CNN-based framework for Alzheimer\u2019s disease detection from EEG via dynamic mode decomposition. Front. Neuroinform., 19.","DOI":"10.3389\/fninf.2025.1706099"},{"key":"ref_457","doi-asserted-by":"crossref","unstructured":"Kapoor, M., Kapoor, M., Shukla, R., and Raj Singh, T. (2021). Early Diagnosis of Alzheimer\u2019s Disease using Machine Learning Based Methods. Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing (IC3-2021), ACM. IC3 \u201921.","DOI":"10.1145\/3474124.3474134"},{"key":"ref_458","first-page":"1","article-title":"A Novel Approach of Diffusion Tensor Visualization Based Neuro Fuzzy Classification System for Early Detection of Alzheimer\u2019s Disease","volume":"3","author":"Kar","year":"2018","journal-title":"J. Alzheimer\u2019s Dis. Rep."},{"key":"ref_459","doi-asserted-by":"crossref","unstructured":"Kayasandik, C.B., Velioglu, H.A., and Hanoglu, L. (2022). Predicting the Effects of Repetitive Transcranial Magnetic Stimulation on Cognitive Functions in Patients with Alzheimer\u2019s Disease by Automated EEG Analysis. Front. Cell. Neurosci., 16.","DOI":"10.3389\/fncel.2022.845832"},{"key":"ref_460","doi-asserted-by":"crossref","first-page":"8000","DOI":"10.1016\/j.jksuci.2022.07.016","article-title":"Development of a three tiered cognitive hybrid machine learning algorithm for effective diagnosis of Alzheimer\u2019s disease","volume":"34","author":"Khan","year":"2022","journal-title":"J. King Saud Univ.\u2014Comput. Inf. Sci."},{"key":"ref_461","doi-asserted-by":"crossref","first-page":"110858","DOI":"10.1016\/j.knosys.2023.110858","article-title":"Adazd-Net: Automated adaptive and explainable Alzheimer\u2019s disease detection system using EEG signals","volume":"278","author":"Khare","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_462","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1007\/s11682-015-9448-7","article-title":"Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer\u2019s disease","volume":"10","author":"Khazaee","year":"2015","journal-title":"Brain Imaging Behav."},{"key":"ref_463","doi-asserted-by":"crossref","first-page":"3728","DOI":"10.1002\/hbm.24207","article-title":"Identification of Alzheimer\u2019s disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine","volume":"39","author":"Kim","year":"2018","journal-title":"Hum. Brain Mapp."},{"key":"ref_464","doi-asserted-by":"crossref","unstructured":"Kim, M., Kim, J., Qu, J., Huang, H., Long, Q., Sohn, K.A., Kim, D., and Shen, L. (2021). Interpretable temporal graph neural network for prognostic prediction of Alzheimer\u2019s disease using longitudinal neuroimaging data. Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.","DOI":"10.1109\/BIBM52615.2021.9669504"},{"key":"ref_465","doi-asserted-by":"crossref","first-page":"5252","DOI":"10.1038\/s41598-024-55656-8","article-title":"Electroencephalography-based classification of Alzheimer\u2019s disease spectrum during computer-based cognitive testing","volume":"14","author":"Kim","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_466","doi-asserted-by":"crossref","unstructured":"Kim, S.K., Kim, J.B., Kim, H., Kim, L., and Kim, S.H. (2025). Early Diagnosis of Alzheimer\u2019s Disease in Human Participants Using EEGConformer and Attention-Based LSTM During the Short Question Task. Diagnostics, 15.","DOI":"10.3390\/diagnostics15040448"},{"key":"ref_467","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1109\/TNSRE.2022.3204913","article-title":"EEG-Based Graph Neural Network Classification of Alzheimer\u2019s Disease: An Empirical Evaluation of Functional Connectivity Methods","volume":"30","author":"Klepl","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_468","doi-asserted-by":"crossref","first-page":"3978","DOI":"10.1109\/TNSRE.2023.3321634","article-title":"Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer\u2019s Disease Using EEG Data","volume":"31","author":"Klepl","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_469","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1523\/JNEUROSCI.0697-21.2021","article-title":"Neurophysiological and Brain Structural Markers of Cognitive Frailty Differ from Alzheimer\u2019s Disease","volume":"42","author":"Kocagoncu","year":"2022","journal-title":"J. Neurosci."},{"key":"ref_470","doi-asserted-by":"crossref","unstructured":"Korolev, I.O., Symonds, L.L., and Bozoki, A.C. (2016). Predicting Progression from Mild Cognitive Impairment to Alzheimer\u2019s Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0138866"},{"key":"ref_471","doi-asserted-by":"crossref","unstructured":"Korolev, S., Safiullin, A., Belyaev, M., and Dodonova, Y. (2017). Residual and plain convolutional neural networks for 3D brain MRI classification. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), IEEE.","DOI":"10.1109\/ISBI.2017.7950647"},{"key":"ref_472","doi-asserted-by":"crossref","first-page":"3935","DOI":"10.1007\/s00542-020-04888-5","article-title":"Machine learning technique for early detection of Alzheimer\u2019s disease","volume":"26","author":"Kumari","year":"2020","journal-title":"Microsyst. Technol."},{"key":"ref_473","doi-asserted-by":"crossref","first-page":"5485080","DOI":"10.1155\/2017\/5485080","article-title":"Diagnosis of Alzheimer\u2019s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features","volume":"2017","author":"Lama","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"ref_474","doi-asserted-by":"crossref","first-page":"3948","DOI":"10.1049\/ipr2.12605","article-title":"Diagnose Alzheimer\u2019s disease by combining 3D discrete wavelet transform and 3D moment invariants","volume":"16","author":"Lao","year":"2022","journal-title":"IET Image Process."},{"key":"ref_475","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.1038\/s41598-018-37769-z","article-title":"Predicting Alzheimer\u2019s disease progression using multi-modal deep learning approach","volume":"9","author":"Lee","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_476","doi-asserted-by":"crossref","unstructured":"Lee, G., Kang, B., Nho, K., Sohn, K.A., and Kim, D. (2019). MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework. Front. Genet., 10.","DOI":"10.3389\/fgene.2019.00617"},{"key":"ref_477","doi-asserted-by":"crossref","unstructured":"Lei, B., Chen, S., Ni, D., and Wang, T. (2016). Discriminative Learning for Alzheimer\u2019s Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion. Front. Aging Neurosci., 8.","DOI":"10.3389\/fnagi.2016.00077"},{"key":"ref_478","doi-asserted-by":"crossref","first-page":"101652","DOI":"10.1016\/j.media.2020.101652","article-title":"Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer\u2019s disease","volume":"61","author":"Lei","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_479","doi-asserted-by":"crossref","unstructured":"Li, F., Cheng, D., and Liu, M. (2017). Alzheimer\u2019s disease classification based on combination of multi-model convolutional networks. Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST), IEEE.","DOI":"10.1109\/IST.2017.8261566"},{"key":"ref_480","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.compmedimag.2018.09.009","article-title":"Alzheimer\u2019s disease diagnosis based on multiple cluster dense convolutional networks","volume":"70","author":"Li","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_481","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1109\/JBHI.2018.2839771","article-title":"Detecting Alzheimer\u2019s Disease on Small Dataset: A Knowledge Transfer Perspective","volume":"23","author":"Li","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_482","doi-asserted-by":"crossref","first-page":"e10549","DOI":"10.7717\/peerj.10549","article-title":"Comparison of machine learning approaches for enhancing Alzheimer\u2019s disease classification","volume":"9","author":"Li","year":"2021","journal-title":"PeerJ"},{"key":"ref_483","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/TPAMI.2018.2889096","article-title":"Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer\u2019s Disease Diagnosis Using Structural MRI","volume":"42","author":"Lian","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_484","doi-asserted-by":"crossref","unstructured":"Lin, W., Tong, T., Gao, Q., Guo, D., Du, X., Yang, Y., Guo, G., Xiao, M., Du, M., and Qu, X. (2018). Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer\u2019s Disease Prediction From Mild Cognitive Impairment. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00777"},{"key":"ref_485","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1109\/TBME.2015.2496233","article-title":"Inherent Structure-Based Multiview Learning with Multitemplate Feature Representation for Alzheimer\u2019s Disease Diagnosis","volume":"63","author":"Liu","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_486","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s12021-018-9370-4","article-title":"Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer\u2019s Disease Diagnosis","volume":"16","author":"Liu","year":"2018","journal-title":"Neuroinformatics"},{"key":"ref_487","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.media.2017.10.005","article-title":"Landmark-based deep multi-instance learning for brain disease diagnosis","volume":"43","author":"Liu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_488","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/TCBB.2016.2635144","article-title":"Classification of Alzheimer\u2019s Disease Using Whole Brain Hierarchical Network","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_489","doi-asserted-by":"crossref","unstructured":"Liu, M., Cheng, D., and Yan, W. (2018). Classification of Alzheimer\u2019s Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images. Front. Neuroinform., 12.","DOI":"10.3389\/fninf.2018.00035"},{"key":"ref_490","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TBME.2018.2869989","article-title":"Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer\u2019s Disease Diagnosis","volume":"66","author":"Liu","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_491","doi-asserted-by":"crossref","first-page":"17106","DOI":"10.1038\/s41598-022-20674-x","article-title":"Generalizable deep learning model for early Alzheimer\u2019s disease detection from structural MRIs","volume":"12","author":"Liu","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_492","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, L., Ning, X., Gao, Y., and Wang, D. (2024). Enhancing early Alzheimer\u2019s disease classification accuracy through the fusion of sMRI and rsMEG data: A deep learning approach. Front. Neurosci., 18.","DOI":"10.3389\/fnins.2024.1480871"},{"key":"ref_493","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s40708-022-00165-5","article-title":"A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer\u2019s Disease","volume":"9","author":"Lombardi","year":"2022","journal-title":"Brain Inform."},{"key":"ref_494","doi-asserted-by":"crossref","unstructured":"Long, Z., Li, J., Fan, J., Li, B., Du, Y., Qiu, S., Miao, J., Chen, J., Yin, J., and Jing, B. (2023). Identifying Alzheimer\u2019s disease and mild cognitive impairment with atlas-based multi-modal metrics. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1212275"},{"key":"ref_495","doi-asserted-by":"crossref","first-page":"101924","DOI":"10.1016\/j.artmed.2020.101924","article-title":"Detection of early stages of Alzheimer\u2019s disease based on MEG activity with a randomized convolutional neural network","volume":"107","author":"Nevado","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_496","doi-asserted-by":"crossref","first-page":"5697","DOI":"10.1038\/s41598-018-22871-z","article-title":"Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer\u2019s Disease using structural MR and FDG-PET images","volume":"8","author":"Lu","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_497","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.media.2018.02.002","article-title":"Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer\u2019s disease","volume":"46","author":"Lu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_498","first-page":"30","article-title":"Automatic Detection and Classification of Alzheimer\u2019s Disease from MRI using TANNN","volume":"148","year":"2016","journal-title":"Int. J. Comput. Appl."},{"key":"ref_499","first-page":"100219","article-title":"Optimizing ResNet50 performance using stochastic gradient descent on MRI images for Alzheimer\u2019s disease classification","volume":"11","author":"Mahjoubi","year":"2025","journal-title":"Intell.-Based Med."},{"key":"ref_500","doi-asserted-by":"crossref","unstructured":"Maturana-Candelas, A., G\u00f3mez, C., Poza, J., Pinto, N., and Hornero, R. (2019). EEG Characterization of the Alzheimer\u2019s Disease Continuum by Means of Multiscale Entropies. Entropy, 21.","DOI":"10.3390\/e21060544"},{"key":"ref_501","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.neuroscience.2021.01.002","article-title":"A Transfer Learning Approach for Early Diagnosis of Alzheimer\u2019s Disease on MRI Images","volume":"460","author":"Mehmood","year":"2021","journal-title":"Neuroscience"},{"key":"ref_502","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1002\/hbm.24896","article-title":"Weighted network measures reveal differences between dementia types: An EEG study","volume":"41","author":"Mehraram","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"ref_503","doi-asserted-by":"crossref","unstructured":"Meng, X., Liu, J., Fan, X., Bian, C., Wei, Q., Wang, Z., Liu, W., and Jiao, Z. (2022). Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer\u2019s Disease. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.911220"},{"key":"ref_504","doi-asserted-by":"crossref","first-page":"71840","DOI":"10.1109\/ACCESS.2023.3294618","article-title":"DICE-Net: A Novel Convolution-Transformer Architecture for Alzheimer Detection in EEG Signals","volume":"11","author":"Miltiadous","year":"2023","journal-title":"IEEE Access"},{"key":"ref_505","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1148\/radiol.2015150220","article-title":"Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis","volume":"279","author":"Pijnenburg","year":"2016","journal-title":"Radiology"},{"key":"ref_506","doi-asserted-by":"crossref","first-page":"101910","DOI":"10.1016\/j.compmedimag.2021.101910","article-title":"A predictive framework based on brain volume trajectories enabling early detection of Alzheimer\u2019s disease","volume":"90","author":"Mofrad","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_507","doi-asserted-by":"crossref","unstructured":"Mohammed, A.N. (2024). Detecting Cognitive Decline in Alzheimer\u2019s Disease using Brain Signals: An EEG Based Classification Approach. Proceedings of the 2024 IEEE 4th International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), IEEE.","DOI":"10.1109\/MI-STA61267.2024.10599651"},{"key":"ref_508","doi-asserted-by":"crossref","unstructured":"Morabito, F.C., Campolo, M., Ieracitano, C., Ebadi, J.M., Bonanno, L., Bramanti, A., Desalvo, S., Mammone, N., and Bramanti, P. (2016). Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer\u2019s disease patients from scalp EEG recordings. Proceedings of the 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), IEEE.","DOI":"10.1109\/RTSI.2016.7740576"},{"key":"ref_509","doi-asserted-by":"crossref","unstructured":"Muksimova, S., Umirzakova, S., Baltayev, J., and Cho, Y.I. (2025). Multi-Modal Fusion and Longitudinal Analysis for Alzheimer\u2019s Disease Classification Using Deep Learning. Diagnostics, 15.","DOI":"10.3390\/diagnostics15060717"},{"key":"ref_510","doi-asserted-by":"crossref","first-page":"110095","DOI":"10.1016\/j.compbiomed.2025.110095","article-title":"Advanced convolutional neural network with attention mechanism for Alzheimer\u2019s disease classification using MRI","volume":"190","author":"Muksimova","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_511","doi-asserted-by":"crossref","first-page":"231","DOI":"10.3233\/JAD-230620","article-title":"Plasma Biomarkers as Predictors of Progression to Dementia in Individuals with Mild Cognitive Impairment","volume":"98","author":"Nallapu","year":"2024","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_512","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.ibneur.2022.08.010","article-title":"Ensemble learning using traditional machine learning and deep neural network for diagnosis of Alzheimer\u2019s disease","volume":"13","author":"Nguyen","year":"2022","journal-title":"IBRO Neurosci. Rep."},{"key":"ref_513","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1109\/TMI.2021.3063150","article-title":"Relation-Induced Multi-Modal Shared Representation Learning for Alzheimer\u2019s Disease Diagnosis","volume":"40","author":"Ning","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_514","doi-asserted-by":"crossref","first-page":"105751","DOI":"10.1016\/j.bspc.2023.105751","article-title":"A novel hybrid model in the diagnosis and classification of Alzheimer\u2019s disease using EEG signals: Deep ensemble learning (DEL) approach","volume":"89","author":"Nour","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_515","doi-asserted-by":"crossref","unstructured":"Oliveira, M.J., Ribeiro, P., and Rodrigues, P.M. (2024). Machine Learning-Driven GLCM Analysis of Structural MRI for Alzheimer\u2019s Disease Diagnosis. Bioengineering, 11.","DOI":"10.3390\/bioengineering11111153"},{"key":"ref_516","doi-asserted-by":"crossref","first-page":"102223","DOI":"10.1016\/j.bspc.2020.102223","article-title":"A novel electroencephalography based approach for Alzheimer\u2019s disease and mild cognitive impairment detection","volume":"63","author":"Oltu","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_517","doi-asserted-by":"crossref","first-page":"1650025","DOI":"10.1142\/S0129065716500258","article-title":"Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer\u2019s Disease","volume":"26","author":"Ortiz","year":"2016","journal-title":"Int. J. Neural Syst."},{"key":"ref_518","doi-asserted-by":"crossref","first-page":"67","DOI":"10.2174\/1567205014666170922101135","article-title":"Discriminative Sparse Features for Alzheimer\u2019s Disease Diagnosis Using Multimodal Image Data","volume":"15","author":"Ortiz","year":"2017","journal-title":"Curr. Alzheimer Res."},{"key":"ref_519","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1038\/s41591-021-01348-z","article-title":"Prediction of future Alzheimer\u2019s disease dementia using plasma phospho-tau combined with other accessible measures","volume":"27","author":"Palmqvist","year":"2021","journal-title":"Nat. Med."},{"key":"ref_520","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1001\/jama.2024.13855","article-title":"Blood Biomarkers to Detect Alzheimer Disease in Primary Care and Secondary Care","volume":"332","author":"Palmqvist","year":"2024","journal-title":"JAMA"},{"key":"ref_521","doi-asserted-by":"crossref","unstructured":"Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., and 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.","DOI":"10.3389\/fnins.2020.00259"},{"key":"ref_522","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s43856-023-00295-9","article-title":"Integrated algorithm combining plasma biomarkers and cognitive assessments accurately predicts brain \u03b2-amyloid pathology","volume":"3","author":"Pan","year":"2023","journal-title":"Commun. Med."},{"key":"ref_523","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/s41598-023-50631-1","article-title":"Histogram-based features track Alzheimer\u2019s progression in brain MRI","volume":"14","author":"Pasnoori","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_524","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.patcog.2018.11.027","article-title":"Structured sparsity regularized multiple kernel learning for Alzheimer\u2019s disease diagnosis","volume":"88","author":"Peng","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_525","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1002\/ima.22657","article-title":"Interpretability of deep neural networks used for the diagnosis of Alzheimer\u2019s disease","volume":"32","author":"Pohl","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_526","doi-asserted-by":"crossref","unstructured":"Puri, D., Nalbalwar, S., Nandgaonkar, A., Kachare, P., Rajput, J., and Wagh, A. (2022). Alzheimer\u2019s Disease Detection using Empirical Mode Decomposition and Hjorth parameters of EEG signal. Proceedings of the 2022 International Conference on Decision Aid Sciences and Applications (DASA), IEEE.","DOI":"10.1109\/DASA54658.2022.9765111"},{"key":"ref_527","doi-asserted-by":"crossref","first-page":"658","DOI":"10.18517\/ijaseit.13.2.18252","article-title":"Identification of Alzheimer\u00e2\u20ac\u2122s Disease Using Novel Dual Decomposition Technique and Machine Learning Algorithms from EEG Signals","volume":"13","author":"Puri","year":"2023","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_528","doi-asserted-by":"crossref","first-page":"100336","DOI":"10.1016\/j.dajour.2023.100336","article-title":"A novel optimal wavelet filter banks for automated diagnosis of Alzheimer\u2019s disease and mild cognitive impairment using Electroencephalogram signals","volume":"9","author":"Puri","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_529","doi-asserted-by":"crossref","first-page":"101972","DOI":"10.1016\/j.nicl.2019.101972","article-title":"Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer\u2019s disease","volume":"24","author":"Pusil","year":"2019","journal-title":"NeuroImage Clin."},{"key":"ref_530","first-page":"737","article-title":"Fusion of deep learning models of MRI scans, Mini\u2013Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment","volume":"10","author":"Qiu","year":"2018","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_531","doi-asserted-by":"crossref","first-page":"35","DOI":"10.21037\/jmai-24-51","article-title":"The impact of the orientation of MRI slices on the accuracy of Alzheimer\u2019s disease classification using convolutional neural networks (CNNs)","volume":"7","author":"Ramalho","year":"2024","journal-title":"J. Med. Artif. Intell."},{"key":"ref_532","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s10916-019-1475-2","article-title":"A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer\u2019s Disease Stages Using Resting-State fMRI and Residual Neural Networks","volume":"44","author":"Ramzan","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_533","doi-asserted-by":"crossref","unstructured":"Raza, N., Naseer, A., Tamoor, M., and Zafar, K. (2023). Alzheimer Disease Classification through Transfer Learning Approach. Diagnostics, 13.","DOI":"10.3390\/diagnostics13040801"},{"key":"ref_534","doi-asserted-by":"crossref","unstructured":"Rieke, J., Eitel, F., Weygandt, M., Haynes, J.D., and Ritter, K. (2018). Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer\u2019s Disease. Understanding and Interpreting Machine Learning in Medical Image Computing Applications, Springer International Publishing.","DOI":"10.1007\/978-3-030-02628-8_3"},{"key":"ref_535","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1016\/j.procs.2016.09.236","article-title":"Alzheimer\u2019s Early Prediction with Electroencephalogram","volume":"100","author":"Rodrigues","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_536","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.1109\/JBHI.2021.3069789","article-title":"Lacsogram: A New EEG Tool to Diagnose Alzheimer\u2019s Disease","volume":"25","author":"Rodrigues","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_537","doi-asserted-by":"crossref","first-page":"3447","DOI":"10.1007\/s11571-024-10152-7","article-title":"EEG-based classification of Alzheimer\u2019s disease and frontotemporal dementia: A comprehensive analysis of discriminative features","volume":"18","author":"Rostamikia","year":"2024","journal-title":"Cogn. Neurodyn."},{"key":"ref_538","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s13195-022-01043-2","article-title":"An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-input digital drawing tasks","volume":"14","author":"Ruengchaijatuporn","year":"2022","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_539","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.21917\/ijivp.2017.0234","article-title":"A robust optimized feature set based automatic classification of Alzheimer\u2019s disease from brain mr images using K-NN and adaboost","volume":"8","author":"Kamathe","year":"2018","journal-title":"ICTACT J. Image Video Process."},{"key":"ref_540","doi-asserted-by":"crossref","first-page":"102338","DOI":"10.1016\/j.bspc.2020.102338","article-title":"Early detection of Alzheimer\u2019s disease from EEG signals using Hjorth parameters","volume":"65","author":"Safi","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_541","doi-asserted-by":"crossref","first-page":"100348","DOI":"10.1016\/j.dajour.2023.100348","article-title":"An Alzheimer\u2019s disease classification model using transfer learning Densenet with embedded healthcare decision support system","volume":"9","author":"Saleh","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_542","doi-asserted-by":"crossref","unstructured":"Salunkhe, S., Bachute, M., Gite, S., Vyas, N., Khanna, S., Modi, K., Katpatal, C., and Kotecha, K. (2021). Classification of Alzheimer\u2019s Disease Patients Using Texture Analysis and Machine Learning. Appl. Syst. Innov., 4.","DOI":"10.3390\/asi4030049"},{"key":"ref_543","doi-asserted-by":"crossref","unstructured":"Samanta, K., Sanchez-Bornot, J.M., McClean, P.L., Kaur, D., Prasad, G., Bhattacharyya, S., and Wong-Lin, K. (2025). Graph-BrainConvNet: A One-class GCN-based approach for MCI detection from source-level MEG. Proceedings of the 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC58623.2025.11254716"},{"key":"ref_544","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.nicl.2016.01.002","article-title":"Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer\u2019s disease","volume":"11","author":"Schouten","year":"2016","journal-title":"NeuroImage Clin."},{"key":"ref_545","doi-asserted-by":"crossref","unstructured":"Senanayake, U., Sowmya, A., and Dawes, L. (2018). Deep fusion pipeline for mild cognitive impairment diagnosis. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE.","DOI":"10.1109\/ISBI.2018.8363832"},{"key":"ref_546","doi-asserted-by":"crossref","unstructured":"Senkaya, Y., Kurnaz, C., and Ozbilgin, F. (2025). Enhancing Alzheimer\u2019s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis. Diagnostics, 15.","DOI":"10.3390\/diagnostics15172190"},{"key":"ref_547","doi-asserted-by":"crossref","unstructured":"Shad, H.A., Rahman, Q.A., Asad, N.B., Bakshi, A.Z., Mursalin, S., Reza, M.T., and Parvez, M.Z. (2021). Exploring Alzheimer\u2019s Disease Prediction with XAI in various Neural Network Models. Proceedings of the TENCON 2021\u20142021 IEEE Region 10 Conference (TENCON), IEEE.","DOI":"10.1109\/TENCON54134.2021.9707468"},{"key":"ref_548","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1002\/hbm.25994","article-title":"Spatial\u2013temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram","volume":"43","author":"Shan","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_549","doi-asserted-by":"crossref","first-page":"101663","DOI":"10.1016\/j.compmedimag.2019.101663","article-title":"Hypergraph based multi-task feature selection for multimodal classification of Alzheimer\u2019s disease","volume":"80","author":"Shao","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_550","doi-asserted-by":"crossref","first-page":"100506","DOI":"10.1016\/j.measen.2022.100506","article-title":"A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans","volume":"24","author":"Sharma","year":"2022","journal-title":"Meas. Sens."},{"key":"ref_551","doi-asserted-by":"crossref","unstructured":"Sharma, S., Gupta, S., Gupta, D., Altameem, A., Saudagar, A.K.J., Poonia, R.C., and Nayak, S.R. (2022). HTLML: Hybrid AI Based Model for Detection of Alzheimer\u2019s Disease. Diagnostics, 12.","DOI":"10.3390\/diagnostics12081833"},{"key":"ref_552","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/JBHI.2017.2655720","article-title":"Multimodal Neuroimaging Feature Learning with Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer\u2019s Disease","volume":"22","author":"Shi","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_553","doi-asserted-by":"crossref","unstructured":"Shmulev, Y., and Belyaev, M. (2018). Predicting Conversion of Mild Cognitive Impairments to Alzheimer\u2019s Disease and Exploring Impact of Neuroimaging. Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities, Springer International Publishing.","DOI":"10.1007\/978-3-030-00689-1_9"},{"key":"ref_554","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.3233\/JAD-210064","article-title":"PET Imaging of Tau Pathology and Amyloid-\u03b2, and MRI for Alzheimer\u2019s Disease Feature Fusion and Multimodal Classification","volume":"84","author":"Shojaie","year":"2021","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_555","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s40846-023-00787-y","article-title":"Structural MRI Texture Analysis for Detecting Alzheimer\u2019s Disease","volume":"43","author":"Silva","year":"2023","journal-title":"J. Med. Biol. Eng."},{"key":"ref_556","doi-asserted-by":"crossref","first-page":"1966","DOI":"10.1109\/TNSRE.2020.3013429","article-title":"A New Framework for Automatic Detection of Patients with Mild Cognitive Impairment Using Resting-State EEG Signals","volume":"28","author":"Siuly","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_557","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1109\/TETCI.2024.3353610","article-title":"Exploring Rhythms and Channels-Based EEG Biomarkers for Early Detection of Alzheimer\u2019s Disease","volume":"8","author":"Siuly","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_558","doi-asserted-by":"crossref","unstructured":"Slimi, H., Balti, A., Abid, S., and Sayadi, M. (2024). A combinatorial deep learning method for Alzheimer\u2019s disease classification-based merging pretrained networks. Front. Comput. Neurosci., 18.","DOI":"10.3389\/fncom.2024.1444019"},{"key":"ref_559","doi-asserted-by":"crossref","first-page":"101940","DOI":"10.1016\/j.jksuci.2024.101940","article-title":"Classification of Alzheimer\u2019s disease using MRI data based on Deep Learning Techniques","volume":"36","author":"Sorour","year":"2024","journal-title":"J. King Saud Univ.\u2014Comput. Inf. Sci."},{"key":"ref_560","doi-asserted-by":"crossref","unstructured":"Stefanou, K., Tzimourta, K.D., Bellos, C., Stergios, G., Markoglou, K., Gionanidis, E., Tsipouras, M.G., Giannakeas, N., Tzallas, A.T., and Miltiadous, A. (2025). A Novel CNN-Based Framework for Alzheimer\u2019s Disease Detection Using EEG Spectrogram Representations. J. Pers. Med., 15.","DOI":"10.3390\/jpm15010027"},{"key":"ref_561","doi-asserted-by":"crossref","first-page":"2569","DOI":"10.1007\/s00429-015-1059-y","article-title":"Deep sparse multi-task learning for feature selection in Alzheimer\u2019s disease diagnosis","volume":"221","author":"Suk","year":"2015","journal-title":"Brain Struct. Funct."},{"key":"ref_562","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.neuroimage.2016.01.005","article-title":"State-space model with deep learning for functional dynamics estimation in resting-state fMRI","volume":"129","author":"Suk","year":"2016","journal-title":"NeuroImage"},{"key":"ref_563","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.media.2017.01.008","article-title":"Deep ensemble learning of sparse regression models for brain disease diagnosis","volume":"37","author":"Suk","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_564","doi-asserted-by":"crossref","first-page":"6961346","DOI":"10.1155\/2023\/6961346","article-title":"Deep Learning-Based Ensembling Technique to Classify Alzheimer\u2019s Disease Stages Using Functional MRI","volume":"2023","author":"Tajammal","year":"2023","journal-title":"J. Healthc. Eng."},{"key":"ref_565","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1016\/j.mri.2016.05.001","article-title":"Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer\u2019s disease","volume":"34","author":"Tang","year":"2016","journal-title":"Magn. Reson. Imaging"},{"key":"ref_566","doi-asserted-by":"crossref","first-page":"9715","DOI":"10.21037\/apm-21-2013","article-title":"Comparing different algorithms for the course of Alzheimer\u2019s disease using machine learning","volume":"10","author":"Tang","year":"2021","journal-title":"Ann. Palliat. Med."},{"key":"ref_567","doi-asserted-by":"crossref","unstructured":"Taqi, A.M., Awad, A., Al-Azzo, F., and Milanova, M. (2018). The Impact of Multi-Optimizers and Data Augmentation on TensorFlow Convolutional Neural Network Performance. Proceedings of the 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), IEEE.","DOI":"10.1109\/MIPR.2018.00032"},{"key":"ref_568","doi-asserted-by":"crossref","unstructured":"Tavares, G., San-Martin, R., Ianof, J.N., Anghinah, R., and Fraga, F.J. (2019). Improvement in the automatic classification of Alzheimer\u2019s disease using EEG after feature selection. Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE.","DOI":"10.1109\/SMC.2019.8914006"},{"key":"ref_569","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.patcog.2016.10.009","article-title":"Multi-modal classification of Alzheimer\u2019s disease using nonlinear graph fusion","volume":"63","author":"Tong","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_570","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/TBME.2016.2549363","article-title":"A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer\u2019s Disease","volume":"64","author":"Tong","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_571","doi-asserted-by":"crossref","first-page":"3743171","DOI":"10.1155\/2020\/3743171","article-title":"Classification of Alzheimer\u2019s Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets","volume":"2020","author":"Toshkhujaev","year":"2020","journal-title":"J. Healthc. Eng."},{"key":"ref_572","doi-asserted-by":"crossref","unstructured":"Trinh, T.T., Tsai, C.F., Hsiao, Y.T., Lee, C.Y., Wu, C.T., and Liu, Y.H. (2021). Identifying Individuals with Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs. Front. Comput. Neurosci., 15.","DOI":"10.3389\/fncom.2021.700467"},{"key":"ref_573","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1007\/s10278-019-00265-5","article-title":"Binary Classification of Alzheimer\u2019s Disease Using sMRI Imaging Modality and Deep Learning","volume":"33","author":"Tufail","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_574","doi-asserted-by":"crossref","first-page":"2649","DOI":"10.1007\/s11063-021-10514-w","article-title":"Classification of Alzheimer\u2019s Disease Using Deep Convolutional Spiking Neural Network","volume":"53","author":"Turkson","year":"2021","journal-title":"Neural Process. Lett."},{"key":"ref_575","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compeleceng.2019.03.018","article-title":"Analysis of electroencephalographic signals complexity regarding Alzheimer\u2019s Disease","volume":"76","author":"Tzimourta","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_576","doi-asserted-by":"crossref","first-page":"119344","DOI":"10.1016\/j.neuroimage.2022.119344","article-title":"A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset","volume":"258","author":"Vaghari","year":"2022","journal-title":"NeuroImage"},{"key":"ref_577","doi-asserted-by":"crossref","first-page":"119054","DOI":"10.1016\/j.neuroimage.2022.119054","article-title":"Late combination shows that MEG adds to MRI in classifying MCI versus controls","volume":"252","author":"Vaghari","year":"2022","journal-title":"NeuroImage"},{"key":"ref_578","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.jneumeth.2019.01.011","article-title":"A Novel Texture Extraction Technique with T1 Weighted MRI for the Classification of Alzheimer\u2019s Disease","volume":"318","author":"Vaithinathan","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_579","doi-asserted-by":"crossref","unstructured":"Valliani, A., and Soni, A. (2017). Deep Residual Nets for Improved Alzheimer\u2019s Diagnosis. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM. BCB \u201917.","DOI":"10.1145\/3107411.3108224"},{"key":"ref_580","doi-asserted-by":"crossref","unstructured":"van Veen, R., Talavera Martinez, L., Kogan, R.V., Meles, S.K., Mudali, D., Roerdink, J.B.T.M., Massa, F., Grazzini, M., Obeso, J.A., and Rodr\u00edguez-Oroz, M.C. (2018). Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases. Applications of Intelligent Systems, IOS Press.","DOI":"10.3233\/978-1-61499-929-4-280"},{"key":"ref_581","doi-asserted-by":"crossref","first-page":"8410","DOI":"10.1038\/s41598-025-92577-6","article-title":"A comprehensive interpretable machine learning framework for mild cognitive impairment and Alzheimer\u2019s disease diagnosis","volume":"15","author":"Vlontzou","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_582","doi-asserted-by":"crossref","unstructured":"Vu, T.D., Yang, H.J., Nguyen, V.Q., Oh, A.R., and Kim, M.S. (2017). Multimodal learning using convolution neural network and Sparse Autoencoder. Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), IEEE.","DOI":"10.1109\/BIGCOMP.2017.7881683"},{"key":"ref_583","doi-asserted-by":"crossref","first-page":"6825","DOI":"10.1007\/s00500-018-3421-5","article-title":"Non-white matter tissue extraction and deep convolutional neural network for Alzheimer\u2019s disease detection","volume":"22","author":"Vu","year":"2018","journal-title":"Soft Comput."},{"key":"ref_584","doi-asserted-by":"crossref","first-page":"10393","DOI":"10.1007\/s11042-016-4222-4","article-title":"Single slice based detection for Alzheimer\u2019s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization","volume":"77","author":"Wang","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_585","doi-asserted-by":"crossref","unstructured":"Wang, S., Shen, Y., Chen, W., Xiao, T., and Hu, J. (2017). Automatic Recognition of Mild Cognitive Impairment from MRI Images Using Expedited Convolutional Neural Networks. Artificial Neural Networks and Machine Learning\u2014ICANN 2017, Springer International Publishing.","DOI":"10.1007\/978-3-319-68600-4_43"},{"key":"ref_586","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, Y., Guo, X., Ye, C., Gao, N., Fang, Y., and Ma, H.T. (2018). A Novel Multimodal MRI Analysis for Alzheimer\u2019s Disease Based on Convolutional Neural Network. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC.2018.8512372"},{"key":"ref_587","doi-asserted-by":"crossref","unstructured":"Wang, X., Cai, W., Shen, D., and Huang, H. (2018). Temporal Correlation Structure Learning for MCI Conversion Prediction. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-00931-1_51"},{"key":"ref_588","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.neucom.2018.12.018","article-title":"Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer\u2019s disease","volume":"333","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_589","doi-asserted-by":"crossref","first-page":"6626728","DOI":"10.1155\/2021\/6626728","article-title":"Assisted Diagnosis of Alzheimer\u2019s Disease Based on Deep Learning and Multimodal Feature Fusion","volume":"2021","author":"Wang","year":"2021","journal-title":"Complexity"},{"key":"ref_590","doi-asserted-by":"crossref","first-page":"111832","DOI":"10.1109\/ACCESS.2023.3323250","article-title":"Functional Brain Network Measures for Alzheimer\u2019s Disease Classification","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_591","doi-asserted-by":"crossref","unstructured":"Wang, R., He, Q., Han, C., Wang, H., Shi, L., and Che, Y. (2023). A deep learning framework for identifying Alzheimer\u2019s disease using fMRI-based brain network. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1177424"},{"key":"ref_592","doi-asserted-by":"crossref","first-page":"181","DOI":"10.37934\/araset.34.3.181191","article-title":"Transfer Learning for Alzheimer\u2019s Disease Diagnosis using EfficientNet-B0 Convolutional Neural Network","volume":"35","author":"Wong","year":"2023","journal-title":"J. Adv. Res. Appl. Sci. Eng. Technol."},{"key":"ref_593","doi-asserted-by":"crossref","first-page":"992","DOI":"10.21037\/qims.2018.10.17","article-title":"Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks","volume":"8","author":"Wu","year":"2018","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_594","doi-asserted-by":"crossref","first-page":"1952373","DOI":"10.1155\/2017\/1952373","article-title":"Brain MR Image Classification for Alzheimer\u2019s Disease Diagnosis Based on Multifeature Fusion","volume":"2017","author":"Xiao","year":"2017","journal-title":"Comput. Math. Methods Med."},{"key":"ref_595","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TBME.2021.3049199","article-title":"A Graph Gaussian Embedding Method for Predicting Alzheimer\u2019s Disease Progression with MEG Brain Networks","volume":"68","author":"Xu","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_596","doi-asserted-by":"crossref","unstructured":"Xu, X., and Yan, X. (2022). A Convenient and Reliable Multi-Class Classification Model based on Explainable Artificial Intelligence for Alzheimer\u2019s Disease. Proceedings of the 2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), IEEE.","DOI":"10.1109\/AEECA55500.2022.9918895"},{"key":"ref_597","doi-asserted-by":"crossref","unstructured":"Yang, S., Bornot, J.M.S., Fernandez, R.B., Deravi, F., Hoque, S., Wong-Lin, K., and Prasad, G. (2021). Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers. Sensors, 21.","DOI":"10.3390\/s21186210"},{"key":"ref_598","doi-asserted-by":"crossref","first-page":"76","DOI":"10.2174\/1567205019666220128120927","article-title":"Random-Forest-Algorithm-Based Applications of the Basic Characteristics and Serum and Imaging Biomarkers to Diagnose Mild Cognitive Impairment","volume":"19","author":"Yang","year":"2022","journal-title":"Curr. Alzheimer Res."},{"key":"ref_599","doi-asserted-by":"crossref","first-page":"3787","DOI":"10.1007\/s00429-015-1132-6","article-title":"Graph-guided joint prediction of class label and clinical scores for the Alzheimer\u2019s disease","volume":"221","author":"Yu","year":"2015","journal-title":"Brain Struct. Funct."},{"key":"ref_600","doi-asserted-by":"crossref","first-page":"108876","DOI":"10.1016\/j.patcog.2022.108876","article-title":"A novel explainable neural network for Alzheimer\u2019s disease diagnosis","volume":"131","author":"Yu","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_601","doi-asserted-by":"crossref","first-page":"108621","DOI":"10.1016\/j.compbiomed.2024.108621","article-title":"Comparative analysis of machine learning algorithms for Alzheimer\u2019s disease classification using EEG signals and genetic information","volume":"176","author":"Yu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_602","doi-asserted-by":"crossref","first-page":"93752","DOI":"10.1109\/ACCESS.2019.2926288","article-title":"Hierarchical Feature Extraction for Early Alzheimer\u2019s Disease Diagnosis","volume":"7","author":"Yue","year":"2019","journal-title":"IEEE Access"},{"key":"ref_603","doi-asserted-by":"crossref","unstructured":"Zhang, J., Stonnington, C., Li, Q., Shi, J., Bauer, R.J., Gutman, B.A., Chen, K., Reiman, E.M., Thompson, P.M., and Ye, J. (2016). Applying sparse coding to surface multivariate tensor-based morphometry to predict future cognitive decline. Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE.","DOI":"10.1109\/ISBI.2016.7493350"},{"key":"ref_604","doi-asserted-by":"crossref","first-page":"855","DOI":"10.3233\/JAD-170069","article-title":"Multivariate Approach for Alzheimer\u2019s Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization","volume":"65","author":"Zhang","year":"2017","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_605","doi-asserted-by":"crossref","first-page":"108795","DOI":"10.1016\/j.jneumeth.2020.108795","article-title":"Multi-modal neuroimaging feature fusion for diagnosis of Alzheimer\u2019s disease","volume":"341","author":"Zhang","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"ref_606","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.inffus.2020.09.002","article-title":"Alzheimer\u2019s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion","volume":"66","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_607","doi-asserted-by":"crossref","first-page":"5289","DOI":"10.1109\/JBHI.2021.3066832","article-title":"An Explainable 3D Residual Self-Attention Deep Neural Network for Joint Atrophy Localization and Alzheimer\u2019s Disease Diagnosis Using Structural MRI","volume":"26","author":"Zhang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_608","doi-asserted-by":"crossref","unstructured":"Zheng, X., Shi, J., Zhang, Q., Ying, S., and Li, Y. (2017). Improving MRI-based diagnosis of Alzheimer\u2019s disease via an ensemble privileged information learning algorithm. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), IEEE.","DOI":"10.1109\/ISBI.2017.7950559"},{"key":"ref_609","first-page":"887","article-title":"Identification of Alzheimer\u2019s Disease and Mild Cognitive Impairment Using Networks Constructed Based on Multiple Morphological Brain Features","volume":"3","author":"Zheng","year":"2018","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"ref_610","doi-asserted-by":"crossref","unstructured":"Zhou, K., He, W., Xu, Y., Xiong, G., and Cai, J. (2018). Feature Selection and Transfer Learning for Alzheimer\u2019s Disease Clinical Diagnosis. Appl. Sci., 8.","DOI":"10.3390\/app8081372"},{"key":"ref_611","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/TBME.2015.2466616","article-title":"Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification","volume":"63","author":"Zhu","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_612","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1007\/s11682-015-9480-7","article-title":"Label-aligned multi-task feature learning for multimodal classification of Alzheimer\u2019s disease and mild cognitive impairment","volume":"10","author":"Zu","year":"2015","journal-title":"Brain Imaging Behav."},{"key":"ref_613","doi-asserted-by":"crossref","first-page":"111065","DOI":"10.1016\/j.compbiomed.2025.111065","article-title":"Regional attention-enhanced vision transformer for accurate Alzheimer\u2019s disease classification using sMRI data","volume":"197","author":"Jomeiri","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_614","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neuroimage.2014.05.018","article-title":"General overview on the merits of multimodal neuroimaging data fusion","volume":"102","author":"Roebroeck","year":"2014","journal-title":"NeuroImage"},{"key":"ref_615","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1002\/mrm.25560","article-title":"Contrast enhancement by combining T1- and T2-weighted structural brain MR Images: Contrast Enhancement with T1w and T2w MRI","volume":"74","author":"Misaki","year":"2014","journal-title":"Magn. Reson. Med."},{"key":"ref_616","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1016\/j.neuroimage.2011.03.082","article-title":"MRI detection of tissue pathology beyond atrophy in Alzheimer\u2019s disease: Introducing T2-VBM","volume":"56","author":"Pereira","year":"2011","journal-title":"NeuroImage"},{"key":"ref_617","doi-asserted-by":"crossref","unstructured":"Tzimourta, K.D., Giannakeas, N., Tzallas, A.T., Astrakas, L.G., Afrantou, T., Ioannidis, P., Grigoriadis, N., Angelidis, P., Tsalikakis, D.G., and Tsipouras, M.G. (2019). EEG Window Length Evaluation for the Detection of Alzheimer\u2019s Disease over Different Brain Regions. Brain Sci., 9.","DOI":"10.3390\/brainsci9040081"},{"key":"ref_618","doi-asserted-by":"crossref","unstructured":"Rodrigues, P.M., Madeiro, J.P., and Marques, J.A.L. (2023). Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices. Bioengineering, 10.","DOI":"10.3390\/bioengineering10070792"},{"key":"ref_619","doi-asserted-by":"crossref","unstructured":"Rodrigues, P., and Teixeira, J.P. (2011). Artificial Neural Networks in the Discrimination of Alzheimer\u2019s Disease. ENTERprise Information Systems, Springer.","DOI":"10.1007\/978-3-642-24352-3_29"},{"key":"ref_620","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.clinph.2004.01.001","article-title":"EEG dynamics in patients with Alzheimer\u2019s disease","volume":"115","author":"Jeong","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"ref_621","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.procs.2018.10.030","article-title":"Electroencephalogram Hybrid Method for Alzheimer Early Detection","volume":"138","author":"Rodrigues","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_622","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1017\/S1041610221001083","article-title":"Slowing on quantitative EEG is associated with transition to dementia in mild cognitive impairment","volume":"33","author":"Hamilton","year":"2021","journal-title":"Int. Psychogeriatr."},{"key":"ref_623","doi-asserted-by":"crossref","first-page":"40","DOI":"10.4018\/IJRQEH.2018010104","article-title":"Electroencephalogram Signal Analysis in Alzheimer\u2019s Disease Early Detection","volume":"7","author":"Rodrigues","year":"2018","journal-title":"Int. J. Reliab. Qual. E-Healthc."},{"key":"ref_624","doi-asserted-by":"crossref","first-page":"539621","DOI":"10.4061\/2011\/539621","article-title":"Slowing and Loss of Complexity in Alzheimer\u2019s EEG: Two Sides of the Same Coin?","volume":"2011","author":"Dauwels","year":"2011","journal-title":"Int. J. Alzheimer\u2019s Dis."},{"key":"ref_625","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1016\/j.protcy.2012.09.095","article-title":"Alzheimer Electroencephalogram Temporal Events Detection by K-means","volume":"5","author":"Rodrigues","year":"2012","journal-title":"Procedia Technol."},{"key":"ref_626","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1103\/RevModPhys.65.413","article-title":"Magnetoencephalography\u2014theory, instrumentation, and applications to noninvasive studies of the working human brain","volume":"65","author":"Hari","year":"1993","journal-title":"Rev. Mod. Phys."},{"key":"ref_627","doi-asserted-by":"crossref","first-page":"107","DOI":"10.4103\/0972-2327.128676","article-title":"Magnetoencephalography: Basic principles","volume":"17","author":"Singh","year":"2014","journal-title":"Ann. Indian Acad. Neurol."},{"key":"ref_628","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1016\/j.neuron.2013.10.017","article-title":"EEG and MEG: Relevance to Neuroscience","volume":"80","author":"LopesdaSilva","year":"2013","journal-title":"Neuron"},{"key":"ref_629","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1111\/j.2517-6161.1951.tb00088.x","article-title":"The Interpretation of Interaction in Contingency Tables","volume":"13","author":"Simpson","year":"1951","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_630","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1093\/ije\/dyr041","article-title":"The Simpson\u2019s paradox unraveled","volume":"40","author":"Clayton","year":"2011","journal-title":"Int. J. Epidemiol."},{"key":"ref_631","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neuroimage.2017.06.061","article-title":"Cross-validation failure: Small sample sizes lead to large error bars","volume":"180","author":"Varoquaux","year":"2018","journal-title":"NeuroImage"},{"key":"ref_632","doi-asserted-by":"crossref","first-page":"22544","DOI":"10.1038\/s41598-021-01681-w","article-title":"Effect of data leakage in brain MRI classification using 2D convolutional neural networks","volume":"11","author":"Yagis","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_633","doi-asserted-by":"crossref","unstructured":"Menezes, J., Barbosa, M.I., and Rodrigues, P.M. (2026). \u03b2-amyloid\u2013stratified six-stage Alzheimer\u2019s disease discrimination via multiband MRI histogram and GLCM features. Biomed. Signal Process. Control, 127.","DOI":"10.1016\/j.bspc.2026.111143"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5489\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T04:30:35Z","timestamp":1788237035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5489"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,29]]},"references-count":633,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175489"],"URL":"https:\/\/doi.org\/10.3390\/s26175489","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,29]]}}}