  Recurrent neural networks (RNNs) were designed for dealing with time-series
data and have recently been used for creating predictive models from functional
magnetic resonance imaging (fMRI) data. However, gathering large fMRI datasets
for learning is a difficult task. Furthermore, network interpretability is
unclear. To address these issues, we utilize multitask learning and design a
novel RNN-based model that learns to discriminate between classes while
simultaneously learning to generate the fMRI time-series data. Employing the
long short-term memory (LSTM) structure, we develop a discriminative model
based on the hidden state and a generative model based on the cell state. The
addition of the generative model constrains the network to learn functional
communities represented by the LSTM nodes that are both consistent with the
data generation as well as useful for the classification task. We apply our
approach to the classification of subjects with autism vs. healthy controls
using several datasets from the Autism Brain Imaging Data Exchange. Experiments
show that our jointly discriminative and generative model improves
classification learning while also producing robust and meaningful functional
communities for better model understanding.
