时变动态贝叶斯网络模型及其在皮层脑电网络连接中的应用

Time-varying dynamic Bayesian network model and its application to brain connectivity using electrocorticograph

大脑执行语言的发音需要顶叶、颞叶、额叶等多个脑区协同完成.皮层脑电具有高时间分辨率、较高空间分辨率和高信噪比等优势,为研究大脑的电生理特性提供了重要的技术手段.为了探索大脑对语言的动态处理过程,利用多尺度皮层脑电(标准电极与微电极)分析了被试在执行音节朗读任务时的皮层脑电信号的高频gamma段特征,提出采用时变动态贝叶斯网络构建单次实验任务的有向网络.结果显示该方法能够快速有效地构建语言任务过程中标准电极、微电极以及二者之间的有向网络连接,且反映了大规模网络(标准电极之间的连接)、局部网络(微电极之间的连接)以及大规模网络与局部网络之间的连接(标准电极与微电极之间的连接)随语言任务发生的动态改变.研究还发现,发音时刻之前与之后的网络连接存在显著性差异,且发音方式不同的音节网络间也存在明显差异.该研究将有助于癫痫等神经疾病的术前临床评估以及理解大脑对语言加工的实时处理过程.

Cortical networks for speech production are believed to be widely distributed and highly organized over temporal,parietal, and frontal lobes areas in the human brain cortex. Effective connectivity demonstrates an inherent element of directional information propagation, and is therefore an information dense measure for the relevant activity over different cortical regions. Connectivity analysis of electrocorticographic（ECo G） recordings has been widely studied for its excellent signal-to-noise ratio as well as high temporal and spatial resolutions, providing an important approach to human electrophysiological researches. In this paper, we evaluate two patients undergoing invasive monitoring for seizure localization, in which both micro-electrode and standard clinical electrodes are used for ECoG recordings from speechrelated cortical areas during syllable reading test. In order to explore the dynamics of speech processing, we extract the high gamma frequency band（70–110 Hz） power from ECo G signals by the multi-taper method. The trial-averaged results show that there is a consistent task-related increase in high gamma response for micro-ECoG electrodes for patient 1 and standard-ECo G electrodes for both patients 1 and 2. We demonstrate that high gamma response provides reliable speech localization compared with electrocortical stimulation. In addition, a directed connectivity network is built in single trial involving both standard ECoG electrodes and micro-ECoG arrays using time-varying dynamic Bayesian networks（TVDBN）. The TV-DBN is used to model the time-varying effective connectivity between pairs of ECoG electrodes selected by high gamma power, with less parameter optimization required and higher computational simplicity than short-time direct directed transfer function. We observe task-related connectivity modulations of connectivity between large-scale cortical networks（standard ECoG） and local cortical networks（micro-ECoG）, as well as between large-scale and local cortical networks. In addition, cortical connectivity is modulated differently before and after response articulation onset.In other words, electrodes located over sensorimotor cortex show higher connectivity before articulation onset, while connectivity appears gradually between sensorimotor and auditory cortex after articulation onset. Also, the connectivity patterns observed during articulation are significantly different for three different places of articulation for the consonants.This study offers insights into preoperative evaluation during epilepsy surgery, dynamic real-time brain connectivity visualization, and assistance to understand the dynamic processing of language pronunciation in the language cortex.