  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.
