Abstract: In order to study the differences in brain function networks of recognition memories with different prior memory groups, the phase transfer entropy method is introduced to analyze the brain function networks of 36 Chinese medical students and 37 Chinese ordinary students when performing picture recognition tasks. The EEG signal is converted into instantaneous phase data by Hilbert transform. The marginal entropy and joint entropy are calculated by the method of combining phase space binning and trial collapsing. Then the phase transfer entropy between the electrodes is calculated according to the definition of phase transfer entropy. The electrodes are used as the network nodes and the phase transfer entropy as the edges of the network, this paper analyzes the network with the complex network method. The results show that in the frequency range of 20~30?Hz, the node out-strength, node in-strength, local efficiency and global efficiency（P<0.05） of medical students are lower than ordinary students. In the frontal lobe, the network hub strength of medical students is lower than ordinary students, between the left temporal lobe and right occipital lobe, the brain information flow of medical students is opposite to that of ordinary students. Compared with traditional methods, the brain network can dig out richer difference information. The results provide support for the study of brain networks in recognition memory.

Key words: phase transfer entropy, recognition memory, brain network, a priori memory, complex network

摘要： 为研究具有不同先验记忆群体的再认记忆脑功能网络差异，引入相位转移熵方法，分析36名中国医学生与37名中国非医学生执行图片再认任务时的脑功能网络。将脑电信号经希尔伯特变换转为瞬时相位数据，使用相空间分箱和实验折叠相结合的方法计算出边际熵和联合熵项，根据相位转移熵定义计算导联之间的相位转移熵，以导联为网络节点，将相位转移熵作为网络的边，并结合复杂网络方法对网络进行分析。结果发现，在20~30?Hz频率范围内，医学生的节点出强度、入强度、局部效率和全局效率（[P]<0.05）低于非医学生；在额叶部位，医学生的网络枢纽强度均小于非医学生；在左颞叶和右枕叶之间，医学生的大脑信息流向与非医学生相反。相比传统方法，脑网络能够挖掘出更丰富的差异信息。该结果为再认记忆的脑网络研究提供支持。

关键词: 相位转移熵, 再认记忆, 脑网络, 先验记忆, 复杂网络