DeepRetinotopy: Predicting the Functional Organization of Human Visual Cortex from Structural MRI Data using Geometric Deep Learning

Fernanda L. Ribeiro, Steffen Bollmann, Alexander M. Puckett

Abstract: Whether it be in a man-made machine or a biological system, form and function are often directly related. In the latter, however, this particular relationship is often unclear due to the intricate nature of biology. Here we developed a geometric deep learning model capable of exploiting the actual structure of the cortex to learn the complex relationship between brain function and anatomy from structural and functional MRI data. Our model was not only able to predict the functional organization of human visual cortex from anatomical properties alone, but it was also able to predict nuanced variations across individuals.

Paper Type: well-validated application

TL;DR: Geometric deep learning was used to model the relationship between the functional organization of human visual cortex and the underlying anatomy.

Track: short paper

Keywords: fMRI, retinotopy, visual hierarchy, cortical folding, manifold, surface model

Presentation Upload Agreement: I agree that my presentation material (videos and slides) will be made public.