Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called generative methods, which generate new data with a probabilistic model by learning sample distribution from real examples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.

翻译：生成对抗网络（GANs）是一类强大的深度学习模型，已成功应用于多个领域。它们属于更广泛的生成方法家族，通过从真实样本中学习数据分布，利用概率模型生成新数据。在临床环境中，与传统生成方法相比，GANs在捕捉空间复杂、非线性及潜在细微的疾病效应方面展现出更强的能力。本综述评估了现有关于GANs在多种神经系统疾病（包括阿尔茨海默病、脑肿瘤、脑衰老及多发性硬化症）影像学研究中应用的文献。我们针对每项应用直观阐释了多种GAN方法，并进一步讨论了将GANs应用于神经影像学的主要挑战、开放性问题及有前景的未来方向。通过阐明GANs如何支持临床决策并促进对脑部疾病结构与功能模式的深入理解，我们旨在弥合先进深度学习方法与神经病学研究之间的鸿沟。