  Support vector machine (SVM) based multivariate pattern analysis (MVPA) has
delivered promising performance in decoding specific task states based on
functional magnetic resonance imaging (fMRI) of the human brain.
Conventionally, the SVM-MVPA requires careful feature selection/extraction
according to expert knowledge. In this study, we propose a deep neural network
(DNN) for directly decoding multiple brain task states from fMRI signals of the
brain without any burden for feature handcrafts. We trained and tested the DNN
classifier using task fMRI data from the Human Connectome Project's S1200
dataset (N=1034). In tests to verify its performance, the proposed
classification method identified seven tasks with an average accuracy of 93.7%.
We also showed the general applicability of the DNN for transfer learning to
small datasets (N=43), a situation encountered in typical neuroscience
research. The proposed method achieved an average accuracy of 89.0% and 94.7%
on a working memory task and a motor classification task, respectively, higher
than the accuracy of 69.2% and 68.6% obtained by the SVM-MVPA. A network
visualization analysis showed that the DNN automatically detected features from
areas of the brain related to each task. Without incurring the burden of
handcrafting the features, the proposed deep decoding method can classify brain
task states highly accurately, and is a powerful tool for fMRI researchers.
