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Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance. 

An important goal in neuroscience has been to map the surface of the human brain, and many researchers have developed sophisticated methods to parcellate the cortex. However, many of these methods stop short of developing a framework to apply existing cortical maps to new subjects in a consistent fashion. The computationally complex step is often the initial mapping of a large set of brains, and it is inefficient to repeat these processes for every new data sample. In this analysis, we propose the use of a library of training brains to build a statistical model of the parcellated cortical surface and to act as templates for mapping new data. We train classifiers on training data sampled from local neighborhoods on the cortical surface, using features derived from training brain connectivity information, and apply these classifiers to map the surfaces of previously unseen brains. We demonstrate the performance of 3 different classifiers, each trained on 3 different types of training features, to accurately predict the map of new brain surfaces.