Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heterogeneous patterns of functional network differences using a demographic-guided attention (DGA) mechanism for recurrent neural network models for prediction from functional magnetic resonance imaging (fMRI) time-series data. The context computed from the DGA head is used to help focus on the appropriate functional networks based on individual demographic information. We demonstrate improved classification on 3 subsets of the ABIDE I dataset used in published studies that have previously produced state-of-the-art results, evaluating performance under a leave-one-site-out cross-validation framework for better generalizeability to new data. Finally, we provide examples of interpreting functional network differences based on individual demographic variables.
@article{arxiv.2104.07654,
title = {Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity},
author = {Nicha C. Dvornek and Xiaoxiao Li and Juntang Zhuang and Pamela Ventola and James S. Duncan},
journal= {arXiv preprint arXiv:2104.07654},
year = {2021}
}