Title: Interpretable LSTMs For Whole-Brain Neuroimaging Analyses,
Abstract: The analysis of neuroimaging data poses several strong challenges, in
particular, due to its high dimensionality, its strong spatio-temporal
correlation and the comparably small sample sizes of the respective datasets.
To address these challenges, conventional decoding approaches such as the
searchlight reduce the complexity of the decoding problem by considering local
clusters of voxels only. Thereby, neglecting the distributed spatial patterns
of brain activity underlying many cognitive states. In this work, we introduce
the DLight framework, which overcomes these challenges by utilizing a long
short-term memory unit (LSTM) based deep neural network architecture to analyze
the spatial dependency structure of whole-brain fMRI data. In order to maintain
interpretability of the neuroimaging data, we adapt the layer-wise relevance
propagation (LRP) method. Thereby, we enable the neuroscientist user to study
the learned association of the LSTM between the data and the cognitive state of
the individual. We demonstrate the versatility of DLight by applying it to a
large fMRI dataset of the Human Connectome Project. We show that the decoding
performance of our method scales better with large datasets, and moreover
outperforms conventional decoding approaches, while still detecting
physiologically appropriate brain areas for the cognitive states classified. We
also demonstrate that DLight is able to detect these areas on several levels of
data granularity (i.e., group, subject, trial, time point).