CAJ | 학술논문

将网络信息的概念引入到神经科学当中对于研究脑功能机制有着积极的作用。然而人脑网络的复杂性对于理解有一定的困难。该文基于有向传递函数(Directed Transfer Function, DTF)的方法估计得到功能连接模式，进一步提出了信息流增益的计算方法，用以评价特定脑区在全脑信息传输过程中的作用。该方法将流入信息和流出信息结合，具有浓缩两者信息的优点，简化了脑复杂网络的辨识度，并且提高了结果的显示标度。仿真运算和自发、诱发脑电数据的结果都显示出通过计算分析信息流增益可以比较理想地得到各个脑区对全脑信息流的贡献。结果证明信息流增益方法为进一步理解大脑认知机制提供了可能。
장망락신식적개념인입도신경과학당중대우연구뇌공능궤제유착적겁적작용。연이인뇌망락적복잡성대우리해유일정적곤난。해문기우유향전체함수(Directed Transfer Function, DTF)적방법고계득도공능련접모식，진일보제출료신식류증익적계산방법，용이평개특정뇌구재전뇌신식전수과정중적작용。해방법장류입신식화류출신식결합，구유농축량자신식적우점，간화료뇌복잡망락적변식도，병차제고료결과적현시표도。방진운산화자발、유발뇌전수거적결과도현시출통과계산분석신식류증익가이비교이상지득도각개뇌구대전뇌신식류적공헌。결과증명신식류증익방법위진일보리해대뇌인지궤제제공료가능。
It has a positive effect on the research of brain function to introduce the concept of network into neuroscience. However, in the real application the brain network with complex characteristics makes it hard to understand. In this paper, based on the functional connectivity patterns estimated by the Directed Transfer Function (DTF) methods, flow gain is proposed to assess the role of the specific brain region involved in the information transmission process. Integrating input and output information simultaneously, flow gain simplifies the identification of complex networks, as well as improves the display scale of the results. Both the simulation and spontaneous, evoked ElectroEncephaloGram (EEG) data indicate that flow gain can describe the output intensity of specific region to the whole brain. The results prove that with the definition of flow gain, it is possible to further the understanding of brain cognitive mechanism.