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

TL;DR

This paper introduces a geometric deep learning model that predicts the functional organization of the human visual cortex from structural MRI data, capturing individual variations and advancing understanding of brain structure-function relationships.

Contribution

The study presents a novel geometric deep learning approach that directly models cortical structure to predict functional organization, a significant step beyond traditional correlation-based methods.

Findings

Successfully predicted visual cortex organization from anatomy

Captured individual differences in functional brain maps

Demonstrated the model's ability to generalize across subjects

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.