  Decoding brain functional states underlying different cognitive processes
using multivariate pattern recognition techniques has attracted increasing
interests in brain imaging studies. Promising performance has been achieved
using brain functional connectivity or brain activation signatures for a
variety of brain decoding tasks. However, most of existing studies have built
decoding models upon features extracted from imaging data at individual time
points or temporal windows with a fixed interval, which might not be optimal
across different cognitive processes due to varying temporal durations and
dependency of different cognitive processes. In this study, we develop a deep
learning based framework for brain decoding by leveraging recent advances in
sequence modeling using long short-term memory (LSTM) recurrent neural networks
(RNNs). Particularly, functional profiles extracted from task functional
imaging data based on their corresponding subject-specific intrinsic functional
networks are used as features to build brain decoding models, and LSTM RNNs are
adopted to learn decoding mappings between functional profiles and brain
states. We evaluate the proposed method using task fMRI data from the HCP
dataset, and experimental results have demonstrated that the proposed method
could effectively distinguish brain states under different task events and
obtain higher accuracy than conventional decoding models.
