On the benefits of self-taught learning for brain decoding
This work addresses brain decoding for neuroimaging researchers, but it is incremental as it applies an existing self-taught learning method to a new domain.
The study tackled the problem of improving brain decoding on new tasks by using a large public neuroimaging database in a self-taught learning framework, resulting in improved classifier performance with benefits varying based on sample sizes and task complexity.
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.