Before revision:  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 statesclassified. We also demonstrate that DLight is able to detect these areas on several levels of data granularity (i.e., group, subject, trial, time point) .
After revision:  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 regions associated with these states. We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples .