  Context. We study the benefits of using a large public neuroimaging database
composed of fMRI statistic maps, in a self-taught learning framework, for
improving brain decoding on new tasks. First, we leverage the NeuroVault
database to train, on a selection of relevant statistic maps, a convolutional
autoencoder to reconstruct these maps. Then, we use this trained encoder to
initialize a supervised convolutional neural network to classify tasks or
cognitive processes of unseen statistic maps from large collections of the
NeuroVault database. Results. We show that such a self-taught learning process
always improves the performance of the classifiers but the magnitude of the
benefits strongly depends on the number of samples available both for
pre-training and finetuning the models and on the complexity of the targeted
downstream task. Conclusion. The pre-trained model improves the classification
performance and displays more generalizable features, less sensitive to
individual differences.
