{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T10:07:44Z","timestamp":1784801264680,"version":"3.55.0"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T00:00:00Z","timestamp":1784505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Autism Spectrum Disorder (ASD) assessment remains challenging because behavioural instruments are partly observer-dependent and neuroimaging data are heterogeneous. This paper presents FAA (Fuse After Aligned), which is a multimodal classification framework that combines transfer learning for structural MRI (sMRI) representation learning with a contrastive objective for the pre-fusion alignment of sMRI and resting-state functional MRI-derived functional connectivity (FC) features. Evaluation was restricted to the single-site ABIDE-I New York University subset comprising 75 participants with ASD and 98 typically developing controls. Under the reported five-fold internal cross-validation protocol, FAA achieved a mean accuracy of 92.6% compared with 87.4% for naive fusion and 90.9% for the sMRI-only baseline. Ablation analyses indicate that adding the contrastive objective is associated with improved classification performance and that ResNet-18 outperforms the evaluated ViT-16 configurations in this small-sample setting. These findings support the methodological value of pre-fusion feature alignment within the evaluated cohort. The framework offers a robust, computationally efficient, and clinically viable approach for objective ASD diagnosis with strong potential for generalisation to multi-site neuroimaging applications.<\/jats:p>","DOI":"10.3390\/jimaging12070328","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T13:18:31Z","timestamp":1784553511000},"page":"328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Contrastive and Transfer Learning for Aligned Multimodal Neuroimaging Classification of Autism Spectrum Disorder"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9120-5607","authenticated-orcid":false,"given":"Raja","family":"Vavekanand","sequence":"first","affiliation":[{"name":"Department of Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi 75660, Sindh, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0098-0948","authenticated-orcid":false,"given":"Ganesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8817-0754","authenticated-orcid":false,"given":"Muhammad Moazzam","family":"Jawaid","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shafiya Qadeer","family":"Memon","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8769-4989","authenticated-orcid":false,"given":"Teerath","family":"Kumar","sequence":"additional","affiliation":[{"name":"School of Computing, Atlantic Technological University, F94 DV52 Letterkenny, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Consoli, C., Turriziani, L., Antoci, M., Lo Monaco, M., Ceraolo, G., Spoto, G., Nicotera, A.G., and Di Rosa, G. (2026). Sensory phenotypes in autism spectrum disorder associated with distinct patterns of social communication, repetitive and restrictive behaviors or interests, and comorbidities: A state-of-the-art review. Brain Sci., 16.","DOI":"10.3390\/brainsci16010053"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tomaszek, N., Urbaniak, A.D., Ba\u0142dyga, D., Chwesiuk, K., Modzelewski, S., and Waszkiewicz, N. (2025). Unraveling the connections: Eating issues, microbiome, and gastrointestinal symptoms in autism spectrum disorder. Nutrients, 17.","DOI":"10.3390\/nu17030486"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"35","DOI":"10.32921\/2225-9929-2023-4-54-35-41","article-title":"The socio-economic burden of autism spectrum disorders: A review of the literature","volume":"4","author":"Kayupova","year":"2023","journal-title":"J. Health Dev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101916","DOI":"10.1016\/j.xcrm.2024.101916","article-title":"Latest clinical frontiers related to autism diagnostic strategies","volume":"6","author":"Cortese","year":"2025","journal-title":"Cell Rep. Med."},{"key":"ref_5","unstructured":"Udhnani, M., Becker, L., and Lecavalier, L. Performance of autism screening and diagnostic instruments among children with ADHD: A systematic review. J. Autism Dev. Disord., Online ahead of print."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s13721-024-00499-6","article-title":"Diagnosis of autism spectrum disorder: A systematic review of clinical and artificial intelligence methods","volume":"14","author":"Taneera","year":"2025","journal-title":"Netw. Model. Anal. Health Inform. Bioinform."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gkintoni, E., Panagioti, M., Vassilopoulos, S.P., Nikolaou, G., Boutsinas, B., and Vantarakis, A. (2025). Leveraging AI-driven neuroimaging biomarkers for early detection and social function prediction in autism spectrum disorders: A systematic review. Healthcare, 13.","DOI":"10.3390\/healthcare13151776"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"277","DOI":"10.3988\/jcn.2025.0185","article-title":"Current clinical applications of structural MRI in neurological disorders","volume":"21","author":"Tae","year":"2025","journal-title":"J. Clin. Neurol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zamani, H., Asghari Varzaneh, Z., and Nadimi-Shahraki, M.H. (2025). Deep convolutional neural networks in neurological disorders diagnosis: Comprehensive review of cutting-edge architectures, challenges, and future directions. Arch. Comput. Methods Eng., Epub ahead of print.","DOI":"10.1007\/s11831-025-10435-0"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s42044-025-00360-7","article-title":"Applications and limitations of large language models to integrate medical context: A comprehensive review","volume":"9","author":"Vavekanand","year":"2026","journal-title":"Iran J. Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, X., Yuan, J., and Chen, Y. (2025). DAGMNet: Dual-branch attention-pruned graph neural network for multimodal sMRI and fMRI fusion in autism prediction. Biomedicines, 13.","DOI":"10.3390\/biomedicines13092168"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"185","DOI":"10.5765\/jkacap.250041","article-title":"Trends in multimodal artificial intelligence for autism research in children and adolescents: A scientometric study","volume":"36","author":"Yan","year":"2025","journal-title":"J. Korean Acad. Child Adolesc. Psychiatry"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"38835","DOI":"10.1038\/s41598-025-09233-2","article-title":"Temporal network instability and low-frequency overconnectivity underlie disorders of consciousness in severe brain injury","volume":"15","author":"Bartolini","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102181","DOI":"10.1016\/j.nicl.2020.102181","article-title":"Improving the detection of autism spectrum disorder by combining structural and functional MRI information","volume":"25","author":"Cabezas","year":"2020","journal-title":"Neuroimage Clin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2603","DOI":"10.1007\/s00530-023-01132-8","article-title":"Early-stage autism diagnosis using action videos and contrastive feature learning","volume":"29","author":"Rani","year":"2023","journal-title":"Multimed. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s10916-023-02032-0","article-title":"Multiple classification of brain MRI autism spectrum disorder by age and gender using deep learning","volume":"48","author":"Nogay","year":"2024","journal-title":"J. Med. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1109\/TNNLS.2020.3007943","article-title":"Identifying autism spectrum disorder from resting-state fMRI using deep belief network","volume":"32","author":"Huang","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12","DOI":"10.53388\/BMEC2026012","article-title":"A lightweight physics-conditioned diffusion multi-model for medical image reconstruction","volume":"5","author":"Vavekanand","year":"2026","journal-title":"Biomed. Eng. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Han, Z., Yang, F., Huang, J., Zhang, C., and Yao, J. (2022). Multimodal dynamics: Dynamical fusion for trustworthy multimodal classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.02005"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e70276","DOI":"10.1111\/ejn.70276","article-title":"Resting-state functional MRI analyses for brain activity characterization: A narrative review of features and methods","volume":"62","author":"Sharma","year":"2025","journal-title":"Eur. J. Neurosci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, Y., and Li, W. (2023). Resizer Swin transformer-based classification using sMRI for Alzheimer\u2019s disease. Appl. Sci., 13.","DOI":"10.20944\/preprints202307.0799.v1"},{"key":"ref_22","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume":"Volume 139","author":"Radford","year":"2021","journal-title":"Proceedings of the 38th International Conference on Machine Learning"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bahathiq, R.A., Banjar, H., Bamaga, A.K., and Alghamdi, A. (2022). Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging. Front. Neuroinform., 16.","DOI":"10.3389\/fninf.2022.949926"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Giansanti, D. (2023). An umbrella review of the fusion of fMRI and AI in autism. Diagnostics, 13.","DOI":"10.3390\/diagnostics13233552"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1038\/s41398-024-03024-5","article-title":"The diagnosis of ASD with MRI: A systematic review and meta-analysis","volume":"14","author":"Schielen","year":"2024","journal-title":"Transl. Psychiatry"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105239","DOI":"10.1016\/j.compbiomed.2022.105239","article-title":"MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis","volume":"142","author":"Wen","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105090","DOI":"10.1016\/j.bspc.2023.105090","article-title":"Autism spectrum disorder diagnosis using the relational graph attention network","volume":"85","author":"Gu","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108065","DOI":"10.1016\/j.cmpb.2024.108065","article-title":"Residual graph transformer for autism spectrum disorder prediction","volume":"247","author":"Wang","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109083","DOI":"10.1016\/j.compbiomed.2024.109083","article-title":"MADE-for-ASD: A multi-atlas deep ensemble network for diagnosing autism spectrum disorder","volume":"182","author":"Liu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Manikantan, K., and Jaganathan, S. (2023). A model for diagnosing autism patients using spatial and statistical measures using rs-fMRI and sMRI by adopting graphical neural networks. Diagnostics, 13.","DOI":"10.3390\/diagnostics13061143"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102475","DOI":"10.1016\/j.artmed.2022.102475","article-title":"DeepMNF: Deep multimodal neuroimaging framework for diagnosing autism spectrum disorder","volume":"136","author":"Abbas","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Alharthi, A.G., and Alzahrani, S.M. (2023). Multi-slice generation sMRI and fMRI for autism spectrum disorder diagnosis using 3D-CNN and vision transformers. Brain Sci., 13.","DOI":"10.3390\/brainsci13111578"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40708-023-00217-4","article-title":"Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders","volume":"11","author":"Saponaro","year":"2024","journal-title":"Brain Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102932","DOI":"10.1016\/j.media.2023.102932","article-title":"A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders","volume":"90","author":"Zhang","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5672","DOI":"10.1002\/hbm.26469","article-title":"Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection","volume":"44","author":"Wang","year":"2023","journal-title":"Hum. Brain Mapp."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kim, Y.-G., Ravid, O., Zheng, X., Kim, Y., Neria, Y., Lee, S., He, X., and Zhu, X. (2024). Explaining deep learning-based representations of resting state functional connectivity data: Focusing on interpreting nonlinear patterns in autism spectrum disorder. Front. Psychiatry, 15.","DOI":"10.3389\/fpsyt.2024.1397093"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1002\/hbm.21333","article-title":"A whole brain fMRI atlas generated via spatially constrained spectral clustering","volume":"33","author":"Craddock","year":"2012","journal-title":"Hum. Brain Mapp."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/mp.2013.78","article-title":"The Autism Brain Imaging Data Exchange: Towards a Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism","volume":"19","author":"Yan","year":"2014","journal-title":"Mol. Psychiatry"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/7\/328\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T09:56:18Z","timestamp":1784800578000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/7\/328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,20]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["jimaging12070328"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12070328","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,20]]}}}