基于神经元集群模型的动态因果模型算法综述

A Review of Dynamic Causal Modelling Based on Neuronal Population Models

大脑是人体最复杂的器官,各个脑区之间的连接构成了复杂的功能性网络系统,脑网络连接特征的变化与脑的生理病理状态密切相关。近年来,脑网络构建与分析算法成为计算神经科学领域的研究热点之一。在众多脑网络分析方法中,动态因果模型(DCM)由于具有生物物理合理性在探索神经网络的有效连接特性中得到了广泛关注。从基本原理、神经元集群模型和应用等方面对DCM进行综述。首先,介绍DCM的基本原理,并以基于卷积和基于电导的两类神经元集群模型为例,对DCM算法的关键组成——神经生物学模型的建模思想、原理及发展进程进行回顾;然后,综述近年来DCM在认知功能和疾病病理等相关神经信号分析领域的应用,指出DCM有望成为探索脑功能整合机制的方法;最后,结合近几年DCM算法的研究进展及其局限性进行总结和展望。

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,since they 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.