{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T18:48:09Z","timestamp":1793472489365,"version":"4.5.0"},"reference-count":111,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Advances in applied machine learning techniques for neuroimaging have encouraged scientists to implement models to diagnose brain disorders such as Alzheimer\u2019s disease at early stages. Predicting the exact stage of Alzheimer\u2019s disease is challenging; however, complex deep learning techniques can precisely manage this. While successful, these complex architectures are difficult to interrogate and computationally expensive. Therefore, using novel, simpler architectures with more efficient pattern extraction capabilities, such as transformers, is of interest to neuroscientists. This study introduced an optimized vision transformer architecture to predict the group membership by separating healthy adults, mild cognitive impairment, and Alzheimer\u2019s brains within the same age group (&gt;75 years) using resting-state functional (rs-fMRI) and structural magnetic resonance imaging (sMRI) data aggressively preprocessed by our pipeline. Our optimized architecture, known as OViTAD is currently the sole vision transformer-based end-to-end pipeline and outperformed the existing transformer models and most state-of-the-art solutions. Our model achieved F1-scores of 97%\u00b10.0 and 99.55%\u00b10.39 from the testing sets for the rs-fMRI and sMRI modalities in the triple-class prediction experiments. Furthermore, our model reached these performances using 30% fewer parameters than a vanilla transformer. Furthermore, the model was robust and repeatable, producing similar estimates across three runs with random data splits (we reported the averaged evaluation metrics). Finally, to challenge the model, we observed how it handled increasing noise levels by inserting varying numbers of healthy brains into the two dementia groups. Our findings suggest that optimized vision transformers are a promising and exciting new approach for neuroimaging applications, especially for Alzheimer\u2019s disease prediction.<\/jats:p>","DOI":"10.3390\/brainsci13020260","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T02:29:23Z","timestamp":1675650563000},"page":"260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer\u2019s Disease Using Resting-State fMRI and Structural MRI Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3314-4281","authenticated-orcid":false,"given":"Saman","family":"Sarraf","sequence":"first","affiliation":[{"name":"Institute of Electrical and Electronics Engineers, Piscataway, NJ 08854, USA"},{"name":"School of Technology, Northcentral University, San Diego, CA 92123, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arman","family":"Sarraf","sequence":"additional","affiliation":[{"name":"Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6861-5691","authenticated-orcid":false,"given":"Danielle D.","family":"DeSouza","sequence":"additional","affiliation":[{"name":"Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John A. 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Alzheimer\u2019s Dement., 14, 367\u2013429.","DOI":"10.1016\/j.jalz.2018.02.001"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/S1474-4422(17)30159-X","article-title":"Strategic roadmap for an early diagnosis of Alzheimer\u2019s disease based on biomarkers","volume":"16","author":"Frisoni","year":"2017","journal-title":"Lancet Neurol."},{"key":"ref_4","first-page":"123","article-title":"Alzheimer\u2019s disease\u2013why we need early diagnosis","volume":"9","author":"Rasmussen","year":"2019","journal-title":"Degener. Neurol. Neuromuscul. Dis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1038\/nature23002","article-title":"Cryo-EM structures of tau filaments from Alzheimer\u2019s disease","volume":"547","author":"Fitzpatrick","year":"2017","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S1474-4422(16)00067-3","article-title":"Sex differences in Alzheimer\u2019s disease and other dementias","volume":"15","author":"Mazure","year":"2016","journal-title":"Lancet Neurol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.nicl.2015.12.007","article-title":"Regional brain stiffness changes across the Alzheimer\u2019s disease spectrum","volume":"10","author":"Murphy","year":"2016","journal-title":"Neuroimage Clin."},{"key":"ref_8","first-page":"248","article-title":"The incidence of mild cognitive impairment: A systematic review and data synthesis","volume":"11","author":"Gillis","year":"2019","journal-title":"Alzheimer\u2019s Dementia: Diagn. Assess. Dis. Monit."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1038\/s41583-018-0068-2","article-title":"Maintenance, reserve and compensation: The cognitive neuroscience of healthy ageing","volume":"19","author":"Cabeza","year":"2018","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1212\/CON.0000000000000313","article-title":"Mild cognitive impairment","volume":"22","author":"Petersen","year":"2016","journal-title":"Contin. Lifelong Learn. Neurol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1093\/arclin\/acx125","article-title":"A systematic review for functional neuroimaging studies of cognitive reserve across the cognitive aging spectrum","volume":"33","author":"Anthony","year":"2018","journal-title":"Arch. Clin. Neuropsychol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/j.nicl.2018.08.019","article-title":"Structural neuroimaging as clinical predictor: A review of machine learning applications","volume":"20","author":"Dadar","year":"2018","journal-title":"NeuroImage Clin."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.nicl.2017.10.022","article-title":"Neuroimaging and other modalities to assess Alzheimer\u2019s disease in Down syndrome","volume":"17","author":"Neale","year":"2018","journal-title":"NeuroImage Clin."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1002\/ana.25071","article-title":"Age, vascular health, and Alzheimer disease biomarkers in an elderly sample","volume":"82","author":"Vemuri","year":"2017","journal-title":"Ann. Neurol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1038\/d41586-020-01282-z","article-title":"Neuroimaging results altered by varying analysis pipelines, 2020","volume":"582","author":"Lindquist","year":"2020","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13024-020-00395-3","article-title":"Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer\u2019s disease","volume":"15","author":"Wang","year":"2020","journal-title":"Mol. Neurodegener."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"489","DOI":"10.3389\/fneur.2017.00489","article-title":"The bright, artificial intelligence-augmented future of neuroimaging reading","volume":"8","author":"Hainc","year":"2017","journal-title":"Front. Neurol."},{"key":"ref_19","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., 220.","DOI":"10.3389\/fnagi.2019.00220"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"117012","DOI":"10.1016\/j.neuroimage.2020.117012","article-title":"Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline","volume":"219","author":"Henschel","year":"2020","journal-title":"NeuroImage"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Puranik, M., Shah, H., Shah, K., and Bagul, S. (2018, January 14\u201315). Intelligent Alzheimer\u2019s detector using deep learning. Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICCONS.2018.8663065"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.neucom.2018.11.111","article-title":"Computer aided Alzheimer\u2019s disease diagnosis by an unsupervised deep learning technology","volume":"392","author":"Bi","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kazemi, Y., and Houghten, S. (June, January 30). A deep learning pipeline to classify different stages of Alzheimer\u2019s disease from fMRI data. Proceedings of the 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), St. Louis, MO, USA.","DOI":"10.1109\/CIBCB.2018.8404980"},{"key":"ref_24","first-page":"1","article-title":"Interpretable classification of Alzheimer\u2019s disease pathologies with a convolutional neural network pipeline","volume":"10","author":"Tang","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_25","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_26","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_27","doi-asserted-by":"crossref","first-page":"1","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 Informatics"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Song, T.A., Chowdhury, S.R., Yang, F., Jacobs, H., El Fakhri, G., Li, Q., Johnson, K., and Dutta, J. (2019, January 8\u201311). Graph convolutional neural networks for Alzheimer\u2019s disease classification. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759531"},{"key":"ref_29","first-page":"58","article-title":"Recent Applications of Deep Learning Algorithms in Medical Image Analysis","volume":"72","author":"Sarraf","year":"2020","journal-title":"Am. Acad. Sci. Res. J. Eng. Technol. Sci."},{"key":"ref_30","first-page":"1","article-title":"A comprehensive review of deep learning architectures for computer vision applications","volume":"77","author":"Sarraf","year":"2021","journal-title":"Am. Acad. Sci. Res. J. Eng. Technol. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.irbm.2020.06.006","article-title":"Deep convolution neural network based system for early diagnosis of Alzheimer\u2019s disease","volume":"42","author":"Janghel","year":"2021","journal-title":"Irbm"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, J., Wei, X., and Zhang, Q. (2020, January 28\u201330). Alzheimer\u2019s Disease Classification Using Structural MRI Based on Convolutional Neural Networks. Proceedings of the 2020 2nd International Conference on Big-data Service and Intelligent Computation, Johannesburg, South Africa.","DOI":"10.1145\/3440054.3440056"},{"key":"ref_33","first-page":"483","article-title":"Forecasting the progression of Alzheimer\u2019s disease using neural networks and a novel preprocessing algorithm","volume":"5","author":"Albright","year":"2019","journal-title":"Alzheimer\u2019s Dementia: Transl. Res. Clin. Interv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.jneumeth.2019.05.006","article-title":"A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer\u2019s disease","volume":"323","author":"Li","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1007\/s40846-020-00556-1","article-title":"A CNN\u2013RNN\u2013LSTM based amalgamation for Alzheimer\u2019s disease detection","volume":"40","author":"Dua","year":"2020","journal-title":"J. Med. Biol. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-018-1088-1","article-title":"Medical image analysis using convolutional neural networks: A review","volume":"42","author":"Anwar","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.patrec.2018.05.018","article-title":"A review of convolutional-neural-network-based action recognition","volume":"118","author":"Yao","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s13748-019-00203-0","article-title":"Convolutional neural network: A review of models, methodologies and applications to object detection","volume":"9","author":"Dhillon","year":"2020","journal-title":"Prog. Artif. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sornam, M., Muthusubash, K., and Vanitha, V. (2017, January 14\u201316). A survey on image classification and activity recognition using deep convolutional neural network architecture. Proceedings of the 2017 ninth international conference on advanced computing (ICoAC), Chennai, India.","DOI":"10.1109\/ICoAC.2017.8441512"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106062","DOI":"10.1016\/j.knosys.2020.106062","article-title":"Evolution of image segmentation using deep convolutional neural network: A survey","volume":"201","author":"Sultana","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105242","DOI":"10.1016\/j.cmpb.2019.105242","article-title":"Deep learning to detect Alzheimer\u2019s disease from neuroimaging: A systematic literature review","volume":"187","author":"Ebrahimighahnavieh","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_43","first-page":"39","article-title":"Detection of Alzheimer\u2019s disease and dementia states based on deep learning from MRI images: A comprehensive review","volume":"1","author":"Altinkaya","year":"2020","journal-title":"J. Inst. Electron. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Murn, L., Blasi, S., Smeaton, A.F., O\u2019Connor, N.E., and Mrak, M. (2020, January 25\u201328). Interpreting CNN for low complexity learned sub-pixel motion compensation in video coding. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICIP40778.2020.9191193"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"You, J., and Korhonen, J. (2021, January 19\u201322). Transformer for image quality assessment. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506075"},{"key":"ref_46","unstructured":"Li, N., Liu, S., Liu, Y., Zhao, S., and Liu, M. (February, January 27). Neural speech synthesis with transformer network. Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"389","DOI":"10.3233\/JAD-2011-0014","article-title":"Principles of classification analyses in mild cognitive impairment (MCI) and Alzheimer disease","volume":"26","author":"Haller","year":"2011","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_48","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_49","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_50","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1007\/s11682-015-9430-4","article-title":"Canonical feature selection for joint regression and multi-class identification in Alzheimer\u2019s disease diagnosis","volume":"10","author":"Zhu","year":"2016","journal-title":"Brain Imaging Behav."},{"key":"ref_51","unstructured":"Rieke, J., Eitel, F., Weygandt, M., Haynes, J.D., and Ritter, K. (2018). Understanding and Interpreting Machine Learning in Medical Image Computing Applications, Springer."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Farooq, A., Anwar, S., Awais, M., and Rehman, S. (2017, January 18\u201320). A deep CNN based multi-class classification of Alzheimer\u2019s disease using MRI. Proceedings of the 2017 IEEE International Conference on Imaging systems and techniques (IST), Beijing, China.","DOI":"10.1109\/IST.2017.8261460"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Long, X., Chen, L., Jiang, C., Zhang, L., and Initiative, A.D.N. (2017). Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0173372"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sarraf, S., DeSouza, D.D., Anderson, J., and Tofighi, G. (2017). DeepAD: Alzheimer\u2019s disease classification via deep convolutional neural networks using MRI and fMRI. BioRxiv, 070441.","DOI":"10.1101\/070441"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, S., Wang, H., Shen, Y., and Wang, X. (2018, January 17\u201320). Automatic recognition of mild cognitive impairment and alzheimers disease using ensemble based 3d densely connected convolutional networks. Proceedings of the 2018 17th IEEE International conference on machine learning and applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00083"},{"key":"ref_56","unstructured":"Khvostikov, A., Aderghal, K., Benois-Pineau, J., Krylov, A., and Catheline, G. (2018). 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hosseini-Asl, E., Keynton, R., and El-Baz, A. (2016, January 25\u201328). Alzheimer\u2019s disease diagnostics by adaptation of 3D convolutional network. Proceedings of the 2016 IEEE international conference on image processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532332"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"155584","DOI":"10.1109\/ACCESS.2019.2949577","article-title":"MCADNNet: Recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models","volume":"7","author":"Sarraf","year":"2019","journal-title":"IEEE Access"},{"key":"ref_59","unstructured":"Soliman, S.A., Hussein, R.R., El-Dahshan, E.S.A., and Salem, A.B.M. (2020). Innovative Smart Healthcare and Bio-Medical Systems, CRC Press."},{"key":"ref_60","first-page":"125","article-title":"Predicting Alzheimer\u2019s Disease with 3D Convolutional Neural Networks","volume":"10","author":"Soliman","year":"2020","journal-title":"Int. J. Appl. Fuzzy Sets Artif. Intell."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s12021-019-09419-w","article-title":"3D-deep learning based automatic diagnosis of Alzheimer\u2019s disease with joint MMSE prediction using resting-state fMRI","volume":"18","author":"Duc","year":"2020","journal-title":"Neuroinformatics"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.neucom.2020.01.053","article-title":"Detecting Alzheimer\u2019s disease Based on 4D fMRI: An exploration under deep learning framework","volume":"388","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","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":"2020","journal-title":"J. Med. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Sarraf, S., and Tofighi, G. (2016, January 6\u20137). Deep learning-based pipeline to recognize Alzheimer\u2019s disease using fMRI data. Proceedings of the 2016 future technologies conference (FTC), San Francisco, CA, USA.","DOI":"10.1109\/FTC.2016.7821697"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Cheng, D., and Liu, M. (2017, January 18\u201320). Combining convolutional and recurrent neural networks for Alzheimer\u2019s disease diagnosis using PET images. Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China.","DOI":"10.1109\/IST.2017.8261461"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"80893","DOI":"10.1109\/ACCESS.2019.2919385","article-title":"Predicting Alzheimer\u2019s disease using LSTM","volume":"7","author":"Hong","year":"2019","journal-title":"IEEE Access"},{"key":"ref_67","first-page":"1","article-title":"Predictive modeling of the progression of Alzheimer\u2019s disease with recurrent neural networks","volume":"8","author":"Wang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4186666","DOI":"10.1155\/2021\/4186666","article-title":"Classification of Alzheimer\u2019s Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network","volume":"2021","author":"Sethi","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Cui, R., Liu, M., and Li, G. (2018, January 4\u20137). Longitudinal analysis for Alzheimer\u2019s disease diagnosis using RNN. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363833"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"zsz048","DOI":"10.1093\/sleep\/zsz048","article-title":"Obstructive sleep apnea and longitudinal Alzheimer\u2019s disease biomarker changes","volume":"42","author":"Bubu","year":"2019","journal-title":"Sleep"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1533317520918719","DOI":"10.1177\/1533317520918719","article-title":"Longitudinal sensitivity of Alzheimer\u2019s disease severity staging","volume":"35","author":"Benoit","year":"2020","journal-title":"Am. J. Alzheimer\u2019s Dis. Other Dementias\u00ae"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Jabason, E., Ahmad, M.O., and Swamy, M. (2019, January 2\u20135). Hybrid Feature Fusion Using RNN and Pre-trained CNN for Classification of Alzheimer\u2019s Disease (Poster). Proceedings of the 2019 22th International Conference on Information Fusion (FUSION), Ottawa, ON, Canada.","DOI":"10.23919\/FUSION43075.2019.9011301"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3389\/fdgth.2021.637386","article-title":"An effective multimodal image fusion method using MRI and PET for Alzheimer\u2019s disease diagnosis","volume":"3","author":"Song","year":"2021","journal-title":"Front. Digit. Health"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"238","DOI":"10.3389\/fnagi.2020.00238","article-title":"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","volume":"12","author":"Gupta","year":"2020","journal-title":"Front. Aging Neurosci."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Thushara, A., Amma, C.U., John, A., and Saju, R. (2020, January 2\u20134). Multimodal MRI Based Classification and Prediction of Alzheimer\u2019s Disease Using Random Forest Ensemble. Proceedings of the 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), Cochin, India.","DOI":"10.1109\/ACCTHPA49271.2020.9213211"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3389\/fninf.2018.00035","article-title":"Classification of Alzheimer\u2019s disease by combination of convolutional and recurrent neural networks using FDG-PET images","volume":"12","author":"Liu","year":"2018","journal-title":"Front. Neuroinformatics"},{"key":"ref_77","first-page":"1","article-title":"Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer\u2019s disease by meta-analysis and adaptive boosting ensemble learning","volume":"13","author":"Yuen","year":"2021","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_78","first-page":"1","article-title":"Prediction of tau accumulation in prodromal Alzheimer\u2019s disease using an ensemble machine learning approach","volume":"11","author":"Kim","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hu, D. (2019, January 5\u20136). An introductory survey on attention mechanisms in NLP problems. Proceedings of the Proceedings of SAI Intelligent Systems Conference, London, UK.","DOI":"10.1007\/978-3-030-29513-4_31"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Letarte, G., Paradis, F., Gigu\u00e8re, P., and Laviolette, F. (2018, January 1). Importance of self-attention for sentiment analysis. Proceedings of the Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, Brussels, Belgium.","DOI":"10.18653\/v1\/W18-5429"},{"key":"ref_81","first-page":"1","article-title":"Transformer-based deep neural network language models for Alzheimer\u2019s disease risk assessment from targeted speech","volume":"21","author":"Roshanzamir","year":"2021","journal-title":"BMC Med. Informatics Decis. Mak."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Sarasua, I., P\u00f6lsterl, S., Wachinger, C., and Neuroimaging, A.D. (2021, January 27). TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes. Proceedings of the International Workshop on Machine Learning in Medical Imaging, Strasbourg, France.","DOI":"10.1007\/978-3-030-87589-3_22"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhuang, Z., Xuan, K., Qian, D., Xue, Z., Xu, J., Liu, Y., Chai, Y., Zhang, L., and Wang, Q. (2021, January 27). 3DMeT: 3D Medical Image Transformer for Knee Cartilage Defect Assessment. Proceedings of the International Workshop on Machine Learning in Medical Imaging, Strasbourg, France.","DOI":"10.1007\/978-3-030-87589-3_36"},{"key":"ref_84","first-page":"685","article-title":"The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods","volume":"27","author":"Jack","year":"2008","journal-title":"J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Churchill, N.W., Spring, R., Afshin-Pour, B., Dong, F., and Strother, S.C. (2015). An automated, adaptive framework for optimizing preprocessing pipelines in task-based functional MRI. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0145594"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1002\/hbm.21238","article-title":"Optimizing preprocessing and analysis pipelines for single-subject fMRI. I. Standard temporal motion and physiological noise correction methods","volume":"33","author":"Churchill","year":"2012","journal-title":"Hum. Brain Mapp."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.jneumeth.2016.03.001","article-title":"The first step for neuroimaging data analysis: DICOM to NIfTI conversion","volume":"264","author":"Li","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/hbm.10062","article-title":"Fast robust automated brain extraction","volume":"17","author":"Smith","year":"2002","journal-title":"Hum. Brain Mapp."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1006\/nimg.2002.1132","article-title":"Improved optimization for the robust and accurate linear registration and motion correction of brain images","volume":"17","author":"Jenkinson","year":"2002","journal-title":"Neuroimage"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.neuroimage.2010.07.033","article-title":"Unbiased average age-appropriate atlases for pediatric studies","volume":"54","author":"Fonov","year":"2011","journal-title":"Neuroimage"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"S208","DOI":"10.1016\/j.neuroimage.2004.07.051","article-title":"Advances in functional and structural MR image analysis and implementation as FSL","volume":"23","author":"Smith","year":"2004","journal-title":"Neuroimage"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.neubiorev.2015.02.008","article-title":"False positive rates in Voxel-based Morphometry studies of the human brain: Should we be worried?","volume":"52","author":"Scarpazza","year":"2015","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.mri.2007.08.006","article-title":"Effects of spatial smoothing on fMRI group inferences","volume":"26","author":"Mikl","year":"2008","journal-title":"Magn. Reson. Imaging"},{"key":"ref_94","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_95","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_96","unstructured":"Zhou, D., Kang, B., Jin, X., Yang, L., Lian, X., Jiang, Z., Hou, Q., and Feng, J. (2021). Deepvit: Towards deeper vision transformer. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and J\u00e9gou, H. (2021, January 10\u201317). Going deeper with image transformers. Proceedings of the Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1111\/ejn.13717","article-title":"Effects of spatial smoothing on functional brain networks","volume":"46","author":"Glerean","year":"2017","journal-title":"Eur. J. Neurosci."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3389\/fnins.2018.00015","article-title":"Effect of spatial smoothing on task fMRI ICA and functional connectivity","volume":"12","author":"Chen","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_100","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., 777.","DOI":"10.3389\/fnins.2018.00777"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"962","DOI":"10.4103\/1673-5374.233433","article-title":"How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer\u2019s disease: From Alzheimer\u2019s disease neuroimaging initiative (ADNI) database","volume":"13","author":"Dimitriadis","year":"2018","journal-title":"Neural Regen. Res."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.imu.2018.12.003","article-title":"Multistage classifier-based approach for Alzheimer\u2019s disease prediction and retrieval","volume":"14","author":"Kruthika","year":"2019","journal-title":"Informatics Med. Unlocked"},{"key":"ref_103","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_104","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_105","doi-asserted-by":"crossref","first-page":"108701","DOI":"10.1016\/j.jneumeth.2020.108701","article-title":"Deep residual learning for neuroimaging: An application to predict progression to Alzheimer\u2019s disease","volume":"339","author":"Abrol","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"ref_106","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_107","doi-asserted-by":"crossref","first-page":"104879","DOI":"10.1016\/j.compbiomed.2021.104879","article-title":"3D shearlet-based descriptors combined with deep features for the classification of Alzheimer\u2019s disease based on MRI data","volume":"138","author":"Alinsaif","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.compbiomed.2018.09.004","article-title":"Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI","volume":"102","author":"Hojjati","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.01.005","article-title":"RNN-based longitudinal analysis for diagnosis of Alzheimer\u2019s disease","volume":"73","author":"Cui","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jneumeth.2017.12.011","article-title":"Deep learning reveals Alzheimer\u2019s disease onset in MCI subjects: Results from an international challenge","volume":"302","author":"Amoroso","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"ref_111","first-page":"1","article-title":"Detection and Classification of Alzheimer\u2019s disease from cognitive impairment with resting-state fMRI","volume":"1","author":"Buvaneswari","year":"2021","journal-title":"Neural Comput. Appl."}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/13\/2\/260\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:23:31Z","timestamp":1760120611000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/13\/2\/260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,3]]},"references-count":111,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["brainsci13020260"],"URL":"https:\/\/doi.org\/10.3390\/brainsci13020260","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.11.27.470184","asserted-by":"object"}]},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,3]]}}}