Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity

• URL: http://arxiv.org/abs/2104.07654v1
• Date: Thu, 15 Apr 2021 17:58:36 GMT
• Title: Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
• Authors: Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang, Pamela Ventola, and James S. Duncan
• Abstract summary: We propose to model heterogeneous patterns of functional network differences using a demographic-guided attention mechanism. The context computed from the DGA head is used to help focus on the appropriate functional networks based on individual demographic information.
• Score: 13.172419221095252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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