Before revision:  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 .
After revision:  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 functional Magnetic Resonance Imaging (fMRI) data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates the fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the neuroimaging data, we adapt the layer-wise relevance propagation (LRP) method .