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.

翻译：背景：本研究探讨在自我学习框架下，利用包含fMRI统计图谱的大型公共神经影像数据库，以提升新任务脑解码性能的益处。首先，我们基于NeuroVault数据库，在相关统计图谱子集上训练卷积自编码器以重建这些图谱。随后，利用该预训练编码器初始化监督卷积神经网络，对NeuroVault数据库中大量未见过统计图谱的认知任务或认知过程进行分类。结果：研究表明，此类自我学习过程始终能提升分类器性能，但其增益幅度强烈依赖于预训练与微调阶段的有效样本数量，以及目标下游任务的复杂度。结论：预训练模型在提升分类性能的同时，能够提取更具泛化性的特征，降低个体差异敏感性。