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Abstract #1285

Predicting brain function from anatomy with geometric deep learning using high-resolution MRI data

Fernanda Lenita Ribeiro1,2, Steffen Bollmann3, and Alexander M Puckett1,2
1School of Psychology, University of Queensland, Brisbane, Australia, 2Queensland Brain Institute, University of Queensland, Brisbane, Australia, 3Centre for Advanced Imaging, University of Queensland, Brisbane, Australia

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 and involved 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.

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