of Information and Computer, Taiyuan University of Technology

【目的】通过实现脑功能网络的预测,为研究脑功能网络的变化演化规律提供参考。【方法】建立基于对抗生成网络的时序脑功能网络的链路预测模型,通过图卷积和长短期记忆网络分别捕捉脑功能网络的拓扑特征和时序特征,通过全连接层进行特征融合,实现脑功能网络的预测。【结果】所提出的脑功能网络预测方法在两种不同的静息状态功能性磁共振成像数据上,预测AUC为0.95,MAP为0.92,与其他的链路预测模型相比较,该方法在脑功能网络上可以达到较好的预测效果。对脑功能网络的准确预测,在脑网络解码和脑机接口领域具有广泛的应用前景。

【Purposes】 For the purpose of predicting the functional brain network and providing reference for studying the evolution patterns of functional brain network, a model of sequential brain function network based on Generative Adversarial Networks has been built. 【Methods】 The topological and temporal characteristics of brain function network are captured through Graph Convolutional Network and long-term and short-term memory network separately, and through feature fusion in the whole connection layer to realize the prediction of functional brain network. 【Findings】 The accuracy of network prediction with AUC and MAP indicators has been tested. The experimental results show that the AUC and MAP of the proposed method are 0.95 and 0.92, respectively on two different resting state fMRI data. Compared with other link prediction models, this method can achieve better prediction effect on functional brain network. The accu-rate prediction of brain function network owns a wide application prospect in the field of brain network decoding and brain computer interface.

国家自然科学基金资助项目(62176177,61873178,61876124,61906130);山西省国际科技合作项目(201803D421047)

王子健,薛家玥,杨鹏飞,等.基于对抗生成网络的时序脑功能网络预测方法[J].太原理工大学学报,2023,54(5):830-837.

WANG Zijian,XUE Jiayue,YANG Pengfei,et al.A method of link prediction of sequential functional brain net-works based on generative adversarial network[J].Journal of Taiyuan University of Technology,2023,54(5):830-837.