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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 both for pretraining and fine-tuning the models and on the complexity of the targeted downstream task.

Conclusion

The pretrained model improves the classification performance and displays more generalizable features, less sensitive to individual differences.

SUBMITTER: Germani E

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

REPOSITORIES: biostudies-literature

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

Germani Elodie E Fromont Elisa E Maumet Camille C

GigaScience 20221201

<h4>Context</h4>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 c ...[more]

PMID: 37132522

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