Survey on sampling conditioned brain images and imaging measures with generative models

Generative models have become innovative tools across various domains, including neuroscience, where they enable the synthesis of realistic brain imaging data that captures complex anatomical and functional patterns. These models, such as Variational Autoencoders (VAEs), Generative Adversarial Netwo...

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Bibliographic Details
Published in:Biomedical engineering letters Vol. 15; no. 5; pp. 831 - 843
Main Authors: Cheong, Sehyoung, Lee, Hoseok, Kim, Won Hwa
Format: Journal Article
Language:English
Published: Korea The Korean Society of Medical and Biological Engineering 01.09.2025
Springer Nature B.V
Subjects:
Biological and Medical Physics
Biomedical Engineering and Bioengineering
Biomedicine
Biophysics
Brain
Brain research
Conditioning
Data acquisition
Datasets
Deep learning
Diffusion models
Disease
Engineering
Generative adversarial networks
Genetic factors
Image quality
Learning
Machine learning
Medical and Radiation Physics
Medical imaging
Nervous system
Neural networks
Neuroimaging
Neurosciences
Phenotypes
Review Article
Synthetic data
Variables
Conditional generation
Brain imaging
VAE
GAN
Diffusion model
ISSN:2093-9868, 2093-985X, 2093-985X
Online Access:Get full text
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Summary:Generative models have become innovative tools across various domains, including neuroscience, where they enable the synthesis of realistic brain imaging data that captures complex anatomical and functional patterns. These models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, leverage deep learning to generate high-quality brain images while maintaining biological and clinical relevance. These models address critical challenges in brain imaging, e.g., the high cost and time required for data acquisition and the frequent imbalance in datasets, particularly for rare diseases or specific population groups. By conditioning the generative process on variables such as age, sex, clinical phenotypes, or genetic factors, these models enhance dataset diversity and provide opportunities to study underrepresented scenarios, model disease progression, and perform controlled experiments that are otherwise infeasible. Additionally, synthetic data generated by these models offer a potential solution to data privacy concerns, as they provide realistic non-identifiable data. As generative models continue to develop, they hold significant potential to substantially advance neuroscience by augmenting datasets, improving diagnostic accuracy, and accelerating the development of personalized treatments. This paper provides a comprehensive overview of the advancements in generative modeling techniques and their applications in brain imaging, with a particular emphasis on conditional generative methods. By categorizing existing approaches, addressing key challenges, and highlighting future directions, this paper aims to advance the integration of conditional generative models into neuroscience research and clinical workflows.
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ISSN:2093-9868
2093-985X
2093-985X
DOI:10.1007/s13534-025-00487-3