A Review of Latent Representation Models in Neuroimaging
Journal:
arXiv
Published Date:
Dec 24, 2024
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.
