A Review of Latent Representation Models in Neuroimaging
It addresses the problem of managing complex neuroimaging data for researchers and clinicians, but it is incremental as it reviews existing methods without introducing new ones.
This review examines how latent representation models, such as Autoencoders, GANs, and LDMs, are applied to neuroimaging data to reduce complexity and identify patterns related to brain function, with applications in disease diagnosis and fundamental brain mechanisms.
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.