Whether it be in a single neuron or a more complex biological system like the human brain, form and function are often directly related. The functional organization of human visual cortex, for instance, is tightly coupled with the underlying anatomy. This is seen in properties such as cortical magnification (i.e., there is more cortex dedicated to processing foveal vs. peripheral information) as well as in the presence, placement, and connectivity of multiple visual areas - which is critical for the hierarchical processing underpinning the rich experience of human vision. 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 in human visual cortex. We show that our neural network was not only able to predict the functional organization throughout the visual cortical hierarchy, but that it was also able to predict nuanced variations across individuals. Although we demonstrate its utility for modeling the relationship between structure and function in human visual cortex, geometric deep learning is flexible and well-suited for a range of other applications involving data structured in non-Euclidean spaces. ### Competing Interest Statement The authors have declared no competing interest.
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