DOI: 10.5281/zenodo.7046055
title: On the benefits of self-taught learning for brain decoding - Data

description: <strong>DERIVED DATA FROM PAPER "<em>On the benefits of self-taught learning for brain decoding</em>"</strong> Here are stored the data necessary to reproduce the full analysis of the paper "<em>On the benefits of self-taught learning for brain decoding</em>". 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. 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 data available both for pre-training and finetuning the models and on the complexity of the targeted downstream task. <strong>Contents overview </strong> <strong>1. data.zip</strong> <strong>1.1 original</strong> The <strong>original</strong> directory contains 3 subdirectories:<br> - NeuroVault dataset<br> - HCP dataset<br> - BrainPedia dataset Each subdirectory contains:<br> - text files with NeuroVault IDs of statistic maps selected in the global datasets, in the test and validation datasets and in each fold of these datasets ; <br> - csv files corresponding to informations on each statistic map of the datasets (classification labels, subject IDs for split...) ;<br> - an `original` directory in which original statistic maps downloaded from NeuroVault will be stored when executing the `src/download_and_preprocess_data_notebook.ipynb`. <strong>1.2 preprocessed</strong> The <strong>preprocessed</strong> directory contains 3 subdirectories:<br> - NeuroVault dataset<br> - HCP dataset<br> - BrainPedia dataset Each subdirectory contains:<br> - text files with NeuroVault IDs of statistic maps selected in the global datasets, in the test and validation datasets and in each fold of these datasets ; <br> - csv files corresponding to informations on each statistic map of the datasets (classification labels, subject IDs for split...) ;<br> - several subdirectores (`resampled`, `resampled masked`...) in which preprocessed statistic maps will be stored when executing the `src/download_and_preprocess_data_notebook.ipynb`. <strong>1.3 derived</strong> The <strong>derived </strong>directory contains 3 subdirectories:<br> - NeuroVault dataset<br> - HCP dataset<br> - BrainPedia dataset Each subdirectory contains subdirectories in which the parameters of models trained on the different datasets are stored. These subdirectories are named in the following way: <pre><code>{name_of_the_dataset}_maps_classification_{classification_task}_model_cnn_{model_architecture}_valid_{type_of_experiment}_retrain_{type_of_initialization}_{preprocessing_type}_epochs_{number_of_epochs}_batch_size_{batch_size}_lr_{learning_rate}</code></pre> For instance, parameters for the following experiment:<br> - <strong>Dataset</strong>: HCP Dataset subset 50 subjects<br> - <strong>Classification task</strong>: contrast classification<br> - <strong>Model</strong>: 4 layers CNN<br> - <strong>Type of experiment</strong>: Performance evaluation<br> - <strong>Initialization</strong>: Default <br> - <strong>Preprocessing type</strong>: Resampled masked normalized<br> - <strong>Epochs</strong>: 500<br> - <strong>Batch</strong>: 32<br> - <strong>Learning rate</strong>: 1e-04 <br> will be contained in the directory: <pre><code class="language-bash">hcp_dataset_50_maps_classification_contrast_model_cnn_4layers_valid_perf_retrain_no_resampled_masked_normalized_epochs_500_batch_size_32_lr_1e-04</code></pre>