  We propose a method for estimating more reproducible functional networks that
are more strongly associated with dynamic task activity by using recurrent
neural networks with long short term memory (LSTMs). The LSTM model is trained
in an unsupervised manner to learn to generate the functional magnetic
resonance imaging (fMRI) time-series data in regions of interest. The learned
functional networks can then be used for further analysis, e.g., correlation
analysis to determine functional networks that are strongly associated with an
fMRI task paradigm. We test our approach and compare to other methods for
decomposing functional networks from fMRI activity on 2 related but separate
datasets that employ a biological motion perception task. We demonstrate that
the functional networks learned by the LSTM model are more strongly associated
with the task activity and dynamics compared to other approaches. Furthermore,
the patterns of network association are more closely replicated across subjects
within the same dataset as well as across datasets. More reproducible
functional networks are essential for better characterizing the neural
correlates of a target task.
