用潜在表示模型解析脑影像数据，揭示大脑结构与功能的深层规律。
A Review of Latent Representation Models in Neuroimaging
- 通过自编码器、GAN和扩散模型压缩脑影像高维数据到低维潜在空间
- 可识别与年龄、疾病相关的脑结构变化模式及感官信息编码机制
- 适合神经科学、临床诊断与计算神经模型研究者阅读
神经影像数据（如MRI或PET）提供了关于大脑结构与活动的丰富但复杂的信息。为应对这种复杂性，潜在表示模型（如自编码器、生成对抗网络GAN、潜在扩散模型LDM）被广泛应用。这些模型将高维神经影像数据降维至低维潜在空间，以识别与脑功能相关的关键模式与变异。通过建模这些潜在空间，研究人员有望揭示大脑生物学与功能机制，包括脑结构随年龄或疾病的变化，以及其对感官信息的编码、预测与适应新输入的能力。本综述探讨了这些模型在临床应用中的潜力，如疾病诊断与进展监测，也涵盖其在探索基础脑机制（如主动推断与预测编码）中的作用。此类方法为理解与模拟大脑复杂的计算任务提供了有力工具，可能推动认知、感知与神经障碍研究的发展。
原文摘要 · Abstract (English)
Neuroimaging data, particularly from techniques like MRI or PET, offer rich but complex information about brain structure and activity. To manage this complexity, latent representation models - such as Autoencoders, Generative Adversarial Networks (GANs), and Latent Diffusion Models (LDMs) - are increasingly applied. These models are designed to reduce high-dimensional neuroimaging data to lower-dimensional latent spaces, where key patterns and variations related to brain function can be identified. By modeling these latent spaces, researchers hope to gain insights into the biology and function of the brain, including how its structure changes with age or disease, or how it encodes sensory information, predicts and adapts to new inputs. This review discusses how these models are used for clinical applications, like disease diagnosis and progression monitoring, but also for exploring fundamental brain mechanisms such as active inference and predictive coding. These approaches provide a powerful tool for both understanding and simulating the brain's complex computational tasks, potentially advancing our knowledge of cognition, perception, and neural disorders.
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