A Review of Dynamic Causal Modelling Based on Neuronal Population Models
Li Shuangyan1,2,3*, Yue Xuanya1,2,3, Wang Longlong1,2,3, Xu Guizhi1,2,3
1(State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China) 2(Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China) 3(Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin 300130, China)
Abstract：Brain is the most complex organ and plays functional roles through the complex neural networks consisted by the connections in various brain regions. Changes of the network characteristics are closely related to the physiological and pathological states of the brain. In recent years, there has been an increasing focus on brain network analysis algorithms. Among all the methods, dynamic causal modeling (DCM) has received extensive attention due to its biophysical plausibility. This article reviewed advances of DCM from the aspects of basic principles, neuron mass models and applications. After introducing DCM principle, the development of two kinds of neuron mass models: the convolutional based model and the conductance-based model were reviewed, sincethey play key role in the biophysical plausibility of the DCM algorithm. The application examples of DCM in the field of neural signal analysis related to cognitive function and disease pathology were further presented, indicating the effectiveness of DCM. Finally, the research progress and limitations of the DCM algorithm were summarized.
李双燕, 岳宣雅, 王龙龙, 徐桂芝. 基于神经元集群模型的动态因果模型算法综述[J]. 中国生物医学工程学报, 2023, 42(6): 740-749.
Li Shuangyan, Yue Xuanya, Wang Longlong, Xu Guizhi. A Review of Dynamic Causal Modelling Based on Neuronal Population Models. Chinese Journal of Biomedical Engineering, 2023, 42(6): 740-749.
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