The Application Progress of Dynamic Causal Model in Brain Network Research
Liang Sailan1, Wang Duojin1,2 *
1(Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China) 2(Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China)
Abstract：The effective connectivity between different functional areas of the brain is one important research issue in brain science. It is of great significance to investigate the brain networks formed by effective connectivity between brain regions in different situations, which can help people to understand the comprehensive functional mechanism of the brain. This research also has advantages in the treatment of various brain-related diseases and the development of brain functions. Dynamic causal model (DCM) is an advantageous way to analyze effective connectivity in the brain network. In this paper, we reviewed the research on the dynamic causal model based on functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and functional near-infrared spectroscopy (fNIRS). The application of DCM in fMRI can be divided into stroke-related brain networks, cognitive neuroscience brain networks and mental disease related brain networks. The application of DCM in EEG mainly includes cognitive neuroscience and diseases related to schizophrenia, Alzheimer's disease, epilepsy, Parkinson's disease, etc., however, rare in fNIRS so far, is only involved with cognitive neuroscience. Finally, we compared the three technologies and discussed the prospectives.
梁赛兰, 王多琎. 动态因果模型在脑网络研究中的应用进展[J]. 中国生物医学工程学报, 2022, 41(1): 86-99.
Liang Sailan, Wang Duojin. The Application Progress of Dynamic Causal Model in Brain Network Research. Chinese Journal of Biomedical Engineering, 2022, 41(1): 86-99.
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