A Review of Latent Representation Models in Neuroimaging

TL;DR

This review discusses how latent representation models like Autoencoders, GANs, and LDMs are used to analyze complex neuroimaging data, aiding in understanding brain function, disease, and cognition.

Contribution

It provides a comprehensive overview of the application of latent models in neuroimaging, highlighting recent advances and clinical relevance.

Findings

Latent models effectively reduce neuroimaging data complexity.

They enable insights into brain structure, function, and disease.

Models support clinical diagnosis and understanding of brain mechanisms.

Abstract

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.