{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T04:29:14Z","timestamp":1793161754643,"version":"4.5.0"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["62362027"],"award-info":[{"award-number":["62362027"]}]},{"name":"National Nature Science Foundation of China","award":["62362028"],"award-info":[{"award-number":["62362028"]}]},{"name":"National Nature Science Foundation of China","award":["2020YFB2104400"],"award-info":[{"award-number":["2020YFB2104400"]}]},{"name":"National Nature Science Foundation of China","award":["824MS063"],"award-info":[{"award-number":["824MS063"]}]},{"name":"National Nature Science Foundation of China","award":["824MS062"],"award-info":[{"award-number":["824MS062"]}]},{"name":"National Nature Science Foundation of China","award":["Hnjg2023-54"],"award-info":[{"award-number":["Hnjg2023-54"]}]},{"name":"National Key R and D Program of China","award":["62362027"],"award-info":[{"award-number":["62362027"]}]},{"name":"National Key R and D Program of China","award":["62362028"],"award-info":[{"award-number":["62362028"]}]},{"name":"National Key R and D Program of China","award":["2020YFB2104400"],"award-info":[{"award-number":["2020YFB2104400"]}]},{"name":"National Key R and D Program of China","award":["824MS063"],"award-info":[{"award-number":["824MS063"]}]},{"name":"National Key R and D Program of China","award":["824MS062"],"award-info":[{"award-number":["824MS062"]}]},{"name":"National Key R and D Program of China","award":["Hnjg2023-54"],"award-info":[{"award-number":["Hnjg2023-54"]}]},{"name":"Natural Science Foundation of Hainan, China","award":["62362027"],"award-info":[{"award-number":["62362027"]}]},{"name":"Natural Science Foundation of Hainan, China","award":["62362028"],"award-info":[{"award-number":["62362028"]}]},{"name":"Natural Science Foundation of Hainan, China","award":["2020YFB2104400"],"award-info":[{"award-number":["2020YFB2104400"]}]},{"name":"Natural Science Foundation of Hainan, China","award":["824MS063"],"award-info":[{"award-number":["824MS063"]}]},{"name":"Natural Science Foundation of Hainan, China","award":["824MS062"],"award-info":[{"award-number":["824MS062"]}]},{"name":"Natural Science Foundation of Hainan, China","award":["Hnjg2023-54"],"award-info":[{"award-number":["Hnjg2023-54"]}]},{"name":"Program of Graduate Education and Teaching Reform in Hainan, China","award":["62362027"],"award-info":[{"award-number":["62362027"]}]},{"name":"Program of Graduate Education and Teaching Reform in Hainan, China","award":["62362028"],"award-info":[{"award-number":["62362028"]}]},{"name":"Program of Graduate Education and Teaching Reform in Hainan, China","award":["2020YFB2104400"],"award-info":[{"award-number":["2020YFB2104400"]}]},{"name":"Program of Graduate Education and Teaching Reform in Hainan, China","award":["824MS063"],"award-info":[{"award-number":["824MS063"]}]},{"name":"Program of Graduate Education and Teaching Reform in Hainan, China","award":["824MS062"],"award-info":[{"award-number":["824MS062"]}]},{"name":"Program of Graduate Education and Teaching Reform in Hainan, China","award":["Hnjg2023-54"],"award-info":[{"award-number":["Hnjg2023-54"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Background\/Objectives: Structural magnetic resonance imaging (MRI) and 18-fluoro-deoxy-glucose positron emission tomography (PET) reveal the structural and functional information of the brain from different dimensions, demonstrating considerable clinical and practical value in the computer-aided diagnosis of Alzheimer\u2019s disease (AD). However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. Most of the existing methods start from the single-modal data and assume that different datasets meet the same distribution, but they fail to fully consider the complementary information between the multi-modal data and fail to effectively solve the problem of domain distribution difference. Methods: In this study, we propose a multi-modal deep domain adaptation (MM-DDA) model that integrates MRI and PET modal data, which aims to maximize the utilization of the complementarity of the multi-modal data and narrow the differences in domain distribution to boost the accuracy of AD classification. Specifically, MM-DDA comprises three primary modules: (1) the feature encoding module, which employs convolutional neural networks (CNNs) to capture detailed and abstract feature representations from MRI and PET images; (2) the multi-head attention feature fusion module, which is used to fuse MRI and PET features, that is, to capture rich semantic information between modes from multiple angles by dynamically adjusting weights, so as to achieve more flexible and efficient feature fusion; and (3) the domain transfer module, which reduces the distributional discrepancies between the source and target domains by employing adversarial learning training. Results: We selected 639 subjects from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) and considered two transfer learning settings. In ADNI1\u2192ADNI2, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 92.40%, 81.81%, 81.13%, and 85.45%, respectively. In ADNI2\u2192ADNI1, the accuracies of the four experimental groups, AD vs. CN, pMCI vs. sMCI, AD vs. MCI, and MCI vs. CN, reached 94.73%, 81.48%, 85.48%, and 81.69%, respectively. Conclusions: MM-DDA is compared with other deep learning methods on two kinds of transfer learning, and the performance comparison results confirmed the superiority of the proposed method in AD prediction tasks.<\/jats:p>","DOI":"10.3390\/brainsci15060618","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T08:22:34Z","timestamp":1749457354000},"page":"618","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Prediction of Alzheimer\u2019s Disease Based on Multi-Modal Domain Adaptation"],"prefix":"10.3390","volume":"15","author":[{"given":"Binbin","family":"Fu","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China"},{"name":"Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changsong","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China"},{"name":"Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuzu","family":"Liao","sequence":"additional","affiliation":[{"name":"Zhangjiajie People\u2019s Hospital, Zhangjiajie 427000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4593-9332","authenticated-orcid":false,"given":"Fangxiang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China"},{"name":"Division of Biomedical Engineering, Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China"},{"name":"Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e70013","DOI":"10.1002\/trc2.70013","article-title":"Application of the revised criteria for diagnosis and staging of Alzheimer\u2019s disease: Drug development and clinical practice","volume":"10","author":"Jack","year":"2024","journal-title":"Alzheimer\u2019s Dement. Transl. Res. Clin. Interv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"810","DOI":"10.37034\/infeb.v6i4.1046","article-title":"Advancing Alzheimer\u2019s Diagnosis: A Comparative Analysis of Deep Learning Architectures on Multidimensional Health Data","volume":"6","author":"Airlangga","year":"2024","journal-title":"J. Inform. Ekon. Bisnis"},{"key":"ref_3","unstructured":"World Health Organization (2025, March 02). Global Action Plan on the Public Health Response to Dementia 2017\u20132025. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789241513487."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1016\/S1474-4422(20)30314-8","article-title":"Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer\u2019s disease and other dementias","volume":"19","author":"Arbizu","year":"2020","journal-title":"Lancet Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1393724","DOI":"10.3389\/fpsyg.2024.1393724","article-title":"Early indicators of cognitive decline, Alzheimer\u2019s disease, and related dementias captured by neurophysiological tools","volume":"15","author":"Wolf","year":"2024","journal-title":"Front. Psychol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106788","DOI":"10.1016\/j.compbiomed.2023.106788","article-title":"Multimodal cross enhanced fusion network for diagnosis ofAlzheimer\u2019s disease and subjective memory complaints","volume":"157","author":"Leng","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2S","DOI":"10.2967\/jnumed.121.263194","article-title":"18F-FDG PET imaging in neurodegenerative dementing disorders: Insights into subtype classification, emerging disease categories, and mixed dementia with copathologies","volume":"63","author":"Minoshima","year":"2022","journal-title":"J. Nucl. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1515\/revneuro-2023-0033","article-title":"A review of neuroimaging-based data-driven approach for Alzheimer\u2019s disease heterogeneity analysis","volume":"35","author":"Liu","year":"2024","journal-title":"Rev. Neurosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10369","DOI":"10.1007\/s10489-021-03025-7","article-title":"Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification","volume":"52","author":"Zhao","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5346","DOI":"10.1038\/s41467-021-25492-9","article-title":"Accelerated functional brain aging in pre-clinical familial Alzheimer\u2019s disease","volume":"12","author":"Gonneaud","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zheng, B., He, J., Zhu, J., Xie, Y., Zaki, N., and Qin, W. (2023, January 8). Style Enhanced Domain Adaptation Neural Network for Cross-Modality Cervical Tumor Segmentation. Proceedings of the International Workshop on Computational Mathematics Modeling in Cancer Analysis, Cham, Switzerland.","DOI":"10.1007\/978-3-031-45087-7_15"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1007\/s00247-024-06011-6","article-title":"FDG-PET\/MRI in the presurgical evaluation of pediatric epilepsy","volume":"54","author":"Ponisio","year":"2024","journal-title":"Pediatr. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1002\/jmri.29070","article-title":"Image translation for estimating two-dimensional axial amyloid-beta PET from structural MRI","volume":"59","author":"Vega","year":"2024","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.21037\/qims-24-1577","article-title":"Diagnosis of Alzheimer\u2019s disease using transfer learning with multi-modal 3DInception-v4","volume":"15","author":"Yuan","year":"2025","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_15","first-page":"S265","article-title":"Multi-modal Multi-probe PET\/MR Imaging in evaluation of the effect of CSUL surgery in AD patients","volume":"51","author":"Yuan","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108564","DOI":"10.1016\/j.compbiomed.2024.108564","article-title":"HAMMF: Hierarchical attention-based multi-task and multi-modal fusion model for computer-aided diagnosis of Alzheimer\u2019s disease","volume":"176","author":"Liu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s12864-024-09985-7","article-title":"A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks","volume":"25","author":"Wang","year":"2024","journal-title":"BMC Genom."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111676","DOI":"10.1016\/j.knosys.2024.111676","article-title":"Multi-level graph regularized robust multi-modal feature selection for Alzheimer\u2019s disease classification","volume":"293","author":"Zhang","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103213","DOI":"10.1016\/j.media.2024.103213","article-title":"Alzheimer\u2019s disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network","volume":"97","author":"Lei","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108000","DOI":"10.1016\/j.compbiomed.2024.108000","article-title":"Alzheimer\u2019s disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction","volume":"170","author":"Cheng","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101953","DOI":"10.1016\/j.media.2020.101953","article-title":"Incomplete multi-modal representation learning for Alzheimer\u2019s disease diagnosis","volume":"69","author":"Liu","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107483","DOI":"10.1016\/j.bspc.2024.107483","article-title":"Multicentric prototype and pseudo-labeling based source-free domain adaptation for Alzheimer\u2019s disease classification","volume":"103","author":"Zhang","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102076","DOI":"10.1016\/j.media.2021.102076","article-title":"Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification","volume":"71","author":"Guan","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"121016","DOI":"10.1016\/j.neuroimage.2025.121016","article-title":"Adapting to evolving MRI data: A transfer learning approach for Alzheimer\u2019s disease prediction","volume":"307","author":"Turrisi","year":"2025","journal-title":"Neuroimage"},{"key":"ref_25","unstructured":"Dadsetan, S. (2024). Enhancing Alzheimer\u2019s Prognostic Models with Cross-Domain Self-Supervised Learning and MRI Data Harmonization. [Ph.D. Thesis, University of Pittsburgh]."},{"key":"ref_26","unstructured":"(2024, July 09). Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). Available online: https:\/\/ida.loni.usc.edu."},{"key":"ref_27","unstructured":"Mao, A.Q., Mohri, M., and Zhong, Y.T. (2023, January 17\u201323). Cross-entropy loss functions: Theoretical analysis and applications. Proceedings of the International Conference on Machine Learning, Baltimore, MD, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, F., Jiang, M., Liu, F., Xu, D., Fan, Z., and Wang, Y. (2022, January 6\u20138). Classification of heads in multi-head attention mechanisms. Proceedings of the International Conference on Knowledge Science, Engineering and Management, Singapore.","DOI":"10.1007\/978-3-031-10989-8_54"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1109\/TMI.2022.3206042","article-title":"Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution","volume":"42","author":"Zhuang","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3651","DOI":"10.1109\/TMI.2023.3300725","article-title":"Federated domain adaptation via transformer for multi-site Alzheimer\u2019s disease diagnosis","volume":"42","author":"Lei","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/TBME.2023.3294511","article-title":"A novel approach analysing the dynamic brain functional connectivity for improved MCI detection","volume":"71","author":"Cao","year":"2023","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Chen, Y., and Huang, M. (2019, January 8\u201311). Transfer learning with dynamic adversarial adaptation network. Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM), Beijing, China.","DOI":"10.1109\/ICDM.2019.00088"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106570","DOI":"10.1016\/j.compbiomed.2023.106570","article-title":"Prototype-guided multi-scale domain adaptation for Alzheimer\u2019s disease detection","volume":"154","author":"Cai","year":"2023","journal-title":"Comput. Biol. Med."}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/15\/6\/618\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:48:24Z","timestamp":1760032104000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/15\/6\/618"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,7]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["brainsci15060618"],"URL":"https:\/\/doi.org\/10.3390\/brainsci15060618","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,7]]}}}