Geometric deep learning on brain shape predicts sex and age

Abstract

The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about this relationship, and better understand the etiology of a variety of pathologies involving diverse degrees of cortical folding abnormalities. Surface-based approaches have been shown to be particularly efficient in their ability to accurately describe the folded sheet topology of the cortical ribbon. However, the utility of these approaches has been blunted by their reliance on manually defined features in order to capture all relevant geometric properties of cortical folding. In this paper, we propose a deep-learning based method to analyze cortical folding patterns in a data-driven way that alleviates this reliance on manual feature definition. This method builds on the emerging field of geometric deep-learning and uses convolutional neural network architecture adapted to the surface representation of the cortical ribbon. MRI data from 6,410 healthy subjects obtained from 11 publicly available data repositories were used to predict age and sex via brain shape analysis. Ages ranged from 6-89 years. Both inner and outer cortical surfaces were extracted using Freesurfer and then registered into MNI space. Two gCNNs were trained, the first of which to predict subjects self-identified sex, the second of which to predict subjects age. Class Activation Maps (CAM) and Regression Activation Maps (RAM) were constructed to map the topographic distribution of the most influential brain regions involved in the decision process for each gCNN. Using this approach, the gCNN was able to predict a subjects sex with an average accuracy of 87.99% and achieved a Persons coefficient of correlation of 0.93 with an average absolute error 4.58 years when predicting a subjects age.