International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 12, Issue 8 (August 2025), Pages: 255-272

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 Review Paper

Generative AI in neuroscience imaging: A review

 Author(s): 

 Aws I. AbuEid 1, Mohammed Ahmed Elhossiny 2, 3, Marwa Anwar Ibrahim Elghazawy 3, Abdelnasser Saber Mohamed 4, Achraf Ben Miled 4, *, Firas M. Allan 4, Shouki A. Ebad 5, José Escorcia-Gutierrez 6

 Affiliation(s):

 1Faculty of Computer Studies, Arab Open University, Kuwait City, Kuwait
 2Faculty of Specific Education, Mansoura University, Mansoura, Egypt
 3Applied College, Northern Border University, Arar, Saudi Arabia
 4Computer Science Department, Science College, Northern Border University, Arar, Saudi Arabia
 5Center for Scientific Research and Entrepreneurship, Northern Border University, Arar, Saudi Arabia
 6Department of Computational Science and Electronics, Universidad de la Costa, Barranquilla, Colombia

 Full text

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 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0003-1256-2900

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.08.024

 Abstract

Generative AI includes a range of machine learning techniques that model data distributions and generate realistic samples. Methods such as flow-based models, diffusion models, variational autoencoders (VAEs), and generative adversarial networks (GANs) have achieved strong results in various fields. In neuroscience imaging, these techniques can enhance data quality and availability by augmenting datasets, completing missing or noisy data, detecting anomalies, and creating realistic simulations for training predictive models. This review explores the growing role of generative AI in neuroscience imaging, focusing on its applications, benefits, and challenges. It highlights how these models can help overcome data shortages, improve visualization methods, and offer new solutions to persistent problems in the field. By summarizing current research and suggesting directions for future work, this paper aims to support researchers and practitioners in using generative AI to advance neuroscience understanding and improve diagnostic and therapeutic outcomes.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Generative AI, Neuroscience imaging, Data augmentation, Anomaly detection, Predictive modeling

 Article history

 Received 22 February 2025, Received in revised form 23 June 2025, Accepted 3 August 2025

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 AbuEid AI, Elhossiny MA, Elghazawy MAI, Mohamed AS, Ben Miled A, Allan FM, Ebad SA, and Escorcia-Gutierrez J (2025). Generative AI in neuroscience imaging: A review. International Journal of Advanced and Applied Sciences, 12(8): 255-272

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 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4  Fig. 5  

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5  Table 6  Table 7

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 References (54)

  1. Abbasi S, Lan H, Choupan J, Sheikh-Bahaei N, Pandey G, and Varghese B (2024). Deep learning for the harmonization of structural MRI scans: A survey. BioMedical Engineering OnLine, 23: 90. https://doi.org/10.1186/s12938-024-01280-6   [Google Scholar] PMid:39217355 PMCid:PMC11365220
  2. Ahmadi F, Shiri ME, Bidabad B, Sedaghat M, and Memari P (2024). Ricci flow-based brain surface covariance descriptors for diagnosing Alzheimer's disease. Biomedical Signal Processing and Control, 93: 106212. https://doi.org/10.1016/j.bspc.2024.106212   [Google Scholar]
  3. Bacon EJ, He D, Achi NBADA, Wang L, Li H, Yao-Digba PDZ, Monkam P, and Qi S (2024). Neuroimage analysis using artificial intelligence approaches: A systematic review. Medical and Biological Engineering and Computing, 62(9): 2599-2627. https://doi.org/10.1007/s11517-024-03097-w   [Google Scholar] PMid:38664348
  4. Bit S, Dey P, Maji A et al. (2024). MRI-based mild cognitive impairment and Alzheimer's disease classification using an algorithm of combination of variational autoencoder and other machine learning classifiers. Journal of Alzheimer's Disease Reports, 8(1): 1434-1452. https://doi.org/10.1177/25424823241290694   [Google Scholar] PMid:40034356 PMCid:PMC11863754
  5. Bowles C, Chen L, Guerrero R et al. (2018). GAN augmentation: Augmenting training data using generative adversarial networks. Arxiv Preprint Arxiv:1810.10863. https://doi.org/10.48550/arXiv.1810.10863   [Google Scholar]
  6. Bui TD, Nguyen M, Le N, and Luu K (2020). Flow-based deformation guidance for unpaired multi-contrast MRI image-to-image translation. In the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (Part II), Springer International Publishing, Lima, Peru: 728-737. https://doi.org/10.1007/978-3-030-59713-9_70   [Google Scholar]
  7. Cao H, Tan C, Gao Z, Xu Y, Chen G, Heng PA, and Li SZ (2024). A survey on generative diffusion models. IEEE Transactions on Knowledge and Data Engineering, 36(7): 2814-2830. https://doi.org/10.1109/TKDE.2024.3361474   [Google Scholar]
  8. Chadebec C, Thibeau-Sutre E, Burgos N, and Allassonnière S (2022). Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3): 2879-2896. https://doi.org/10.1109/TPAMI.2022.3185773   [Google Scholar] PMid:35749321
  9. Chatterjee S, Sciarra A, Dünnwald M et al. (2022). StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder. Computers in Biology and Medicine, 149: 106093. https://doi.org/10.1016/j.compbiomed.2022.106093   [Google Scholar] PMid:36116318
  10. Chen X, Peng Y, Li D, and Sun J (2023). DMCA-GAN: Dual multilevel constrained attention GAN for MRI-based hippocampus segmentation. Journal of Digital Imaging, 36(6): 2532-2553. https://doi.org/10.1007/s10278-023-00854-5   [Google Scholar] PMid:37735310 PMCid:PMC10584805
  11. Choi KS and Sunwoo L (2022). Artificial intelligence in neuroimaging: Clinical applications. Investigative Magnetic Resonance Imaging, 26(1): 1-9. https://doi.org/10.13104/imri.2022.26.1.1   [Google Scholar]
  12. Fan Y, Liao H, Huang S, Luo Y, Fu H, and Qi H (2024). A survey of emerging applications of diffusion probabilistic models in MRI. Meta-Radiology, 2(2): 100082. https://doi.org/10.1016/j.metrad.2024.100082   [Google Scholar]
  13. Gajjar P, Garg M, Desai S, Chhinkaniwala H, Sanghvi HA, Patel RH, Gupta S, and Pandya AS (2024). An empirical analysis of diffusion, autoencoders, and adversarial deep learning models for predicting dementia using high-fidelity MRI. IEEE Access, 12: 131231-131243. https://doi.org/10.1109/ACCESS.2024.3354724   [Google Scholar]
  14. Gong C, Jing C, Chen X et al. (2023). Generative AI for brain image computing and brain network computing: A review. Frontiers in Neuroscience, 17: 1203104. https://doi.org/10.3389/fnins.2023.1203104   [Google Scholar] PMid:37383107 PMCid:PMC10293625
  15. Goodfellow IJ, Pouget-Abadie J, Mirza M et al. (2014). Generative adversarial nets. Arxiv Preprint Arxiv:1406.2661v1. https://doi.org/10.48550/arXiv.1406.2661   [Google Scholar]
  16. Gupta P, Ding B, Guan C, and Ding D (2024). Generative AI: A systematic review using topic modelling techniques. Data and Information Management, 8(2): 100066. https://doi.org/10.1016/j.dim.2024.100066   [Google Scholar]
  17. Gupta R, Tiwari S, and Chaudhary P (2025). Large generative models for different data types. In: Gupta R, Tiwari S, and Chaudhary P (Eds.), Generative AI: Techniques, models and applications: 103-162. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-031-82062-5_6   [Google Scholar]
  18. Hagos DH, Battle R, and Rawat DB (2024). Recent advances in generative AI and large language models: Current status, challenges, and perspectives. IEEE Transactions on Artificial Intelligence, 5(12): 5873-5893. https://doi.org/10.1109/TAI.2024.3444742   [Google Scholar]
  19. Han C, Rundo L, Araki R, Nagano Y, Furukawa Y, Mauri G, Nakayama H, and Hayashi H (2019). Combining noise-to-image and image-to-image GANs: Brain MR image augmentation for tumor detection. IEEE Access, 7: 156966-156977. https://doi.org/10.1109/ACCESS.2019.2947606   [Google Scholar]
  20. Hwang S and Shin J (2024). Prognosis prediction of Alzheimer’s disease based on multi-modal diffusion model. In the 18th International Conference on Ubiquitous Information Management and Communication, IEEE, Kuala Lumpur, Malaysia: 1-5. https://doi.org/10.1109/IMCOM60618.2024.10418423   [Google Scholar]
  21. Jeevan P, Nixon N, and Sethi A (2024). Normalizing flow-based metric for image generation. Arxiv Preprint Arxiv:2410.02004.  https://doi.org/10.48550/arXiv.2410.02004   [Google Scholar]
  22. Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, and Shi W (2017). Photo-realistic single image super-resolution using a generative adversarial network. In the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA: 4681-4690. https://doi.org/10.1109/CVPR.2017.19   [Google Scholar]
  23. Molnár S and Tamás L (2024). Variational autoencoders for 3D data processing. Artificial Intelligence Review, 57: 42. https://doi.org/10.1007/s10462-023-10687-x   [Google Scholar]
  24. Nalepa J, Marcinkiewicz M, and Kawulok M (2019). Data augmentation for brain-tumor segmentation: A review. Frontiers in Computational Neuroscience, 13: 83. https://doi.org/10.3389/fncom.2019.00083   [Google Scholar] PMid:31920608 PMCid:PMC6917660
  25. Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q, and Shen D (2017). Medical image synthesis with context-aware generative adversarial networks. In the 20th International Conference on Medical Image Computing and Computer Assisted Intervention (Part III), Springer International Publishing, Quebec, Canada: 417-425. https://doi.org/10.1007/978-3-319-66179-7_48   [Google Scholar] PMid:30009283 PMCid:PMC6044459
  26. Nimeshika GN and Subitha D (2024). Enhancing Alzheimer's disease classification through split federated learning and GANs for imbalanced datasets. PeerJ Computer Science, 10: e2459. https://doi.org/10.7717/peerj-cs.2459   [Google Scholar] PMid:39650412 PMCid:PMC11623002
  27. O’Donnell LJ and Schultz T (2015). Statistical and machine learning methods for neuroimaging: Examples, challenges, and extensions to diffusion imaging data. In: Hotz I and Schultz T (Eds.), Visualization and processing of higher order descriptors for multi-valued data: Mathematics and visualization: 299-319. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-15090-1_15   [Google Scholar]
  28. Qiang N, Dong Q, Ge F, Liang H, Ge B, Zhang S, Sun Y, Gao J, and Liu T (2020). Deep variational autoencoder for mapping functional brain networks. IEEE Transactions on Cognitive and Developmental Systems, 13(4): 841-852. https://doi.org/10.1109/TCDS.2020.3025137   [Google Scholar]
  29. Rais K, Amroune M, Benmachiche A, and Haouam MY (2024). Exploring variational autoencoders for medical image generation: A comprehensive study. Arxiv Preprint Arxiv:2411.07348. https://doi.org/10.48550/arXiv.2411.07348   [Google Scholar]
  30. Reaungamornrat S, Sari H, Catana C, and Kamen A (2022). Multimodal image synthesis based on disentanglement representations of anatomical and modality specific features, learned using uncooperative relativistic GAN. Medical Image Analysis, 80: 102514. https://doi.org/10.1016/j.media.2022.102514   [Google Scholar] PMid:35717874 PMCid:PMC9810205
  31. Sabuhi M, Zhou M, Bezemer CP, and Musilek P (2021). Applications of generative adversarial networks in anomaly detection: A systematic literature review. IEEE Access, 9: 161003-161029. https://doi.org/10.1109/ACCESS.2021.3131949   [Google Scholar]
  32. Sajjad M, Ramzan F, Khan MUG, Rehman A, Kolivand M, Fati SM, and Bahaj SA (2021). Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation. Microscopy Research and Technique, 84(12): 3023-3034. https://doi.org/10.1002/jemt.23861   [Google Scholar] PMid:34245203
  33. Seiler M and Ritter K (2025). Pioneering new paths: The role of generative modelling in neurological disease research. Pflügers Archiv-European Journal of Physiology, 477: 571-589. https://doi.org/10.1007/s00424-024-03016-w   [Google Scholar] PMid:39377960 PMCid:PMC11958445
  34. Shin HC, Tenenholtz NA, Rogers JK, Schwarz CG, Senjem ML, Gunter JL, Andriole KP, and Michalski M (2018). Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In the 3rd International Workshop on Simulation and Synthesis in Medical Imaging, Springer International Publishing, Granada, Spain: 1-11. https://doi.org/10.1007/978-3-030-00536-8_1   [Google Scholar] PMid:37312871
  35. Sidulova M and Park CH (2023). Conditional variational autoencoder for functional connectivity analysis of autism spectrum disorder functional magnetic resonance imaging data: A comparative study. Bioengineering, 10(10): 1209. https://doi.org/10.3390/bioengineering10101209   [Google Scholar] PMid:37892939 PMCid:PMC10604768
  36. Singh A and Ogunfunmi T (2021). An overview of variational autoencoders for source separation, finance, and bio-signal applications. Entropy, 24(1): 55. https://doi.org/10.3390/e24010055   [Google Scholar] PMid:35052081 PMCid:PMC8774760
  37. Song W, Wang X, Jiang Y, Li S, Hao A, Hou X, and Qin H (2024). Expressive 3D facial animation generation based on local-to-global latent diffusion. IEEE Transactions on Visualization and Computer Graphics, 30(11): 7397-7407. https://doi.org/10.1109/TVCG.2024.3456213   [Google Scholar] PMid:39255115
  38. Sun H, Mehta R, Zhou HH, Huang Z, Johnson SC, Prabhakaran V, and Singh V (2019). Dual-glow: Conditional flow-based generative model for modality transfer. In the IEEE/CVF International Conference on Computer Vision, IEEE, Seoul, Korea: 10611-10620. https://doi.org/10.1109/ICCV.2019.01071   [Google Scholar] PMid:35125977 PMCid:PMC8813086
  39. Tan YF, Ting CM, Noman F, Phan RCW, and Ombao H (2024). fMRI functional connectivity augmentation using convolutional generative adversarial networks for brain disorder classification. In the IEEE International Symposium on Biomedical Imaging, IEEE, Athens, Greece: 1-5. https://doi.org/10.1109/ISBI56570.2024.10635290   [Google Scholar]
  40. Tomczak JM (2020). General invertible transformations for flow-based generative modeling. Arxiv Preprint Arxiv:2011.15056. https://doi.org/10.48550/arXiv.2011.15056   [Google Scholar]
  41. Wang D, Ma C, and Sun S (2023a). Novel paintings from the latent diffusion model through transfer learning. Applied Sciences, 13(18): 10379. https://doi.org/10.3390/app131810379   [Google Scholar]
  42. Wang R, Bashyam V, Yang Z et al. (2023b). Applications of generative adversarial networks in neuroimaging and clinical neuroscience. Neuroimage, 269: 119898. https://doi.org/10.1016/j.neuroimage.2023.119898   [Google Scholar] PMid:36702211 PMCid:PMC9992336
  43. Wang Y, Solera-Rico A, Vila CS, and Vinuesa R (2024a). Towards optimal β-variational autoencoders combined with transformers for reduced-order modelling of turbulent flows. International Journal of Heat and Fluid Flow, 105: 109254. https://doi.org/10.1016/j.ijheatfluidflow.2023.109254   [Google Scholar]
  44. Wang Z, Li D, Wu Y, He T, Bian J, and Jiang R (2024b). Diffusion models in 3D vision: A survey. Arxiv Preprint Arxiv:2410.04738. https://doi.org/10.48550/arXiv.2410.04738   [Google Scholar]
  45. Wei B, Wen Y, Liu X, Qi X, and Sheng B (2023). SOFNet: Optical-flow based large-scale slice augmentation of brain MRI. Displays, 80: 102536. https://doi.org/10.1016/j.displa.2023.102536   [Google Scholar]
  46. Wei R and Mahmood A (2020). Recent advances in variational autoencoders with representation learning for biomedical informatics: A survey. IEEE Access, 9: 4939-4956. https://doi.org/10.1109/ACCESS.2020.3048309   [Google Scholar]
  47. Xu Z, Qi C, and Xu G (2019). Semi-supervised attention-guided cyclegan for data augmentation on medical images. In the IEEE International Conference on Bioinformatics and Biomedicine, IEEE, San Diego, USA: 563-568. https://doi.org/10.1109/BIBM47256.2019.8982932   [Google Scholar]
  48. Yen C, Lin CL, and Chiang MC (2023). Exploring the frontiers of neuroimaging: A review of recent advances in understanding brain functioning and disorders. Life, 13(7): 1472. https://doi.org/10.3390/life13071472   [Google Scholar] PMid:37511847 PMCid:PMC10381462
  49. Yilmaz B and Korn R (2024). A comprehensive guide to generative adversarial networks (GANs) and application to individual electricity demand. Expert Systems with Applications, 250: 123851. https://doi.org/10.1016/j.eswa.2024.123851   [Google Scholar]
  50. Yin J, Qiao Z, Han L, and Zhang X (2025). EEG-based emotion recognition with autoencoder feature fusion and MSC-TimesNet model. Computer Methods in Biomechanics and Biomedical Engineering. https://doi.org/10.1080/10255842.2025.2477801   [Google Scholar]
  51. Yoon D, Myong Y, Kim YG, Sim Y, Cho M, Oh BM, and Kim S (2024). Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification. NeuroImage, 296: 120663. https://doi.org/10.1016/j.neuroimage.2024.120663   [Google Scholar] PMid:38843963
  52. Yuan C, Duan J, Xu K, Tustison NJ, Hubbard RA, and Linn KA (2024). ReMiND: Recovery of missing neuroimaging using diffusion models with application to Alzheimer's disease. Imaging Neuroscience, 2: 1-14. https://doi.org/10.1162/imag_a_00323   [Google Scholar] PMCid:PMC12290738
  53. Zhen X, Chakraborty R, Yang L, and Singh V (2021). Flow-based generative models for learning manifold to manifold mappings. In the Proceedings of the AAAI Conference on Artificial Intelligence, 35(12): 11042-11052. https://doi.org/10.1609/aaai.v35i12.17318   [Google Scholar] PMid:34457995
  54. Zhou T, Liu M, Thung KH, and Shen D (2019). Latent representation learning for Alzheimer's disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Transactions on Medical Imaging, 38(10): 2411-2422. https://doi.org/10.1109/TMI.2019.2913158   [Google Scholar] PMid:31021792 PMCid:PMC8034601