文献类型：期刊文献

英文题名：BrainNPT: Pre-Training Transformer Networks for Brain Network Classification

作者：Hu, Jinlong[1];Huang, Yangmin[1];Wang, Nan[2,3];Dong, Shoubin[1]

机构：[1]South China Univ Technol, Sch Comp Sci & Engn, Guangdong Key Lab Commun & Comp Network, Guangzhou 510640, Peoples R China;[2]East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200050, Peoples R China;[3]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：32

起止页码：2727

外文期刊名：IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

收录：;EI(收录号：20243216813762);WOS:【SCI-EXPANDED(收录号:WOS:001283728000002)】；

基金：This work was supported in part by the Natural Science Foundation of Guangdong Province of China under Grant 2021A1515011942 and in part by the Innovation Fund of Introduced High-End Scientific Research Institutions of Zhongshan under Grant 2019AG031.

语种：英文

外文关键词：Brain functional networks; transformer; pre-training; classification; Brain functional networks; transformer; pre-training; classification

摘要：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, such as natural language processing. 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 token as a classification embedding vector for the Transformer model to effectively capture the representation of brain networks. Second, we proposed a pre-training framework for BrainNPT model to leverage unlabeled brain network data to learn the structure information of brain functional 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 and the data augmentation methods, analyzed the influence of the parameters of the model, and explained the trained model.