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.

翻译：我们研究了在自教学习框架下，利用由fMRI统计图谱组成的大型公开神经影像数据库，提升新任务大脑解码性能的益处。首先，我们利用NeuroVault数据库，对选定的相关统计图谱训练卷积自编码器以实现图谱重建。随后，利用该预训练编码器初始化有监督卷积神经网络，对NeuroVault数据库中来自大型数据集的未见统计图谱进行任务或认知过程分类。研究表明，自教学习过程始终能提升分类器性能，但收益幅度强烈依赖于预训练与微调阶段可用数据的数量，以及目标下游任务的复杂程度。