Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
This work addresses the challenge of accounting for demographic variability in brain disorder modeling for medical research, representing an incremental improvement with a novel attention mechanism.
The authors tackled the problem of modeling heterogeneous neuropathophysiological patterns in neurological disorders by proposing a demographic-guided attention mechanism for RNNs on fMRI time-series data, achieving improved classification on three ABIDE I subsets with state-of-the-art results under leave-one-site-out cross-validation.
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.