Deep learning methods have advanced quickly in brain imaging analysis over the past few years, but they are usually restricted by the limited labeled data. Pre-trained model on unlabeled data has presented promising improvement in feature learning in many domains, including natural language processing and computer vision. However, this technique is under-explored in brain network analysis. In this paper, we focused on pre-training methods with Transformer networks to leverage existing unlabeled data for brain functional network classification. First, we proposed a Transformer-based neural network, named as BrainNPT, for brain functional network classification. The proposed method leveraged <cls> token as a classification embedding vector for the Transformer model to effectively capture the representation of brain network. Second, we proposed a pre-training framework for BrainNPT model to leverage unlabeled brain network data to learn the structure information of brain networks. The results of classification experiments demonstrated the BrainNPT model without pre-training achieved the best performance with the state-of-the-art models, and the BrainNPT model with pre-training strongly outperformed the state-of-the-art models. The pre-training BrainNPT model improved 8.75% of accuracy compared with the model without pre-training. We further compared the pre-training strategies, analyzed the influence of the parameters of the model, and interpreted the trained model.

翻译：深度学习方法近年来在脑成像分析领域取得了快速进展，但通常受限于有限的标注数据。基于无标注数据的预训练模型已在自然语言处理和计算机视觉等多个领域展现出特征学习方面的显著提升。然而，该技术在脑网络分析中的探索仍不充分。本文聚焦于利用Transformer网络进行预训练的方法，以充分利用现存无标注数据实现脑功能网络分类。首先，我们提出了一种基于Transformer的神经网络——BrainNPT，用于脑功能网络分类。该方法利用<cls>令牌作为Transformer模型的分类嵌入向量，有效捕捉脑网络的表征。其次，我们为BrainNPT模型提出了一种预训练框架，通过利用无标注脑网络数据学习其结构信息。分类实验结果表明，未经过预训练的BrainNPT模型已达到与最先进模型相当的最佳性能，而经过预训练的BrainNPT模型则显著优于现有最优模型。与未预训练模型相比，预训练BrainNPT模型准确率提升了8.75%。此外，我们进一步比较了不同预训练策略，分析了模型参数的影响，并对训练后的模型进行了解释。