On the benefits of self-taught learning for brain decoding.

ABSTRACT:

Context

We study the benefits of using a large public neuroimaging database composed of functional magnetic resonance imaging (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 bot

SUBMITTER: Germani E 

PROVIDER: S-EPMC10155221 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature