Xiang Wentao, Karfoul Ahmad, Shu Huazhong, Le Bouquin Jeannès Régine
INSERM, U1099, Rennes 35000, France; Université de Rennes 1, LTSI, Rennes 35000, France; Centre de Recherche en Information Biomédicale sino-français (CRIBs), 35000, France.
INSERM, U1099, Rennes 35000, France; Université de Rennes 1, LTSI, Rennes 35000, France.
Comput Biol Med. 2017 May 1;84:30-44. doi: 10.1016/j.compbiomed.2017.03.006. Epub 2017 Mar 7.
This paper addresses the question of effective connectivity in the human cerebral cortex in the context of epilepsy. Among model based approaches to infer brain connectivity, spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral density. The proposed strategy we investigated tackles the sub-estimation of the free energy using the well-known variational Expectation-Maximization algorithm highly sensitive to the initialization of the parameters vector by a permanent local adjustment of the initialization process. The performance of the proposed strategy in terms of effective connectivity identification is assessed using simulated data generated by a neuronal mass model (simulating unidirectional and bidirectional flows) and real epileptic intracerebral Electroencephalographic signals. Results show the efficiency of proposed approach compared to the conventional Dynamic Causal Modelling and the one wherein a deterministic annealing scheme is employed.
本文探讨了癫痫背景下人类大脑皮层有效连接性的问题。在基于模型的推断大脑连接性的方法中，频谱动态因果模型是一种传统技术，我们提出了一种替代方法来估计互谱密度。我们研究的所提出策略使用著名的变分期望最大化算法来解决自由能的子估计问题，该算法对参数向量的初始化高度敏感，通过对初始化过程进行永久局部调整来解决。使用由神经元群体模型生成的模拟数据（模拟单向和双向流）和真实癫痫性脑内脑电图信号，评估所提出策略在有效连接性识别方面的性能。结果表明，与传统动态因果模型和采用确定性退火方案的模型相比，所提出的方法具有更高的效率。
