Deep learning (DL) has been extremely successful when applied to the analysis of natural images. By contrast, analyzing neuroimaging data presents some unique challenges, including higher dimensionality, smaller sample sizes, multiple heterogeneous modalities, and a limited ground truth. In this article, we discuss DL methods in the context of four diverse and important categories in the neuroimaging field: classification/prediction, dynamic activity/connectivity, multimodal fusion, and interpretation/visualization. We highlight recent progress in each of these categories, discuss the benefits of combining data characteristics and model architectures, and derive guidelines for the use of DL in neuroimaging data. For each category, we also assess promising applications and major challenges to overcome. Finally, we discuss future directions of neuroimaging DL for clinical applications, a topic of great interest, touching on all four categories.

中文翻译：

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神经影像学中的深度学习：承诺与挑战

深度学习 (DL) 在应用于自然图像分析时非常成功。相比之下，分析神经影像数据提出了一些独特的挑战，包括更高的维度、更小的样本量、多种异构模式和有限的基本事实。在本文中，我们在神经影像领域的四个不同且重要的类别的背景下讨论 DL 方法：分类/预测、动态活动/连接性、多模态融合和解释/可视化。我们重点介绍了每个类别的最新进展，讨论了结合数据特征和模型架构的好处，并得出了在神经影像数据中使用深度学习的指南。对于每个类别，我们还评估有前景的应用和需要克服的主要挑战。最后，