Estimating Reproducible Functional Networks Associated with Task Dynamics using Unsupervised LSTMs
This work addresses the challenge of improving reproducibility in functional network analysis for neuroscience, though it is incremental as it builds on existing methods with a novel application of LSTMs.
The authors tackled the problem of estimating reproducible functional networks from fMRI data by proposing an unsupervised LSTM method, which resulted in networks more strongly associated with task activity and better replication across subjects and datasets.
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.