DeepRetinotopy: Predicting the Functional Organization of Human Visual Cortex from Structural MRI Data using Geometric Deep Learning
This work addresses the challenge of mapping brain function from anatomy for neuroscience and medical applications, representing a novel method for a known bottleneck.
The authors tackled the problem of predicting the functional organization of the human visual cortex from structural MRI data using geometric deep learning, achieving predictions of nuanced individual variations.
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.