关键词: 皮层脑电/
高频gamma/
时变动态贝叶斯网络
English Abstract
Time-varying dynamic Bayesian network model and its application to brain connectivity using electrocorticograph
Guo Miao-Miao1 2,Wang Yu-Jing3 4,
Xu Gui-Zhi1,
Griffin Milsap2,
Nitish V. Thakor2,
Nathan Crone3
1.Department of Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China;
2.Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA;
3.Department of Neurology, Johns Hopkins University, Baltimore, MD 21287, USA;
4.Fischell Department of Bioengineering, University of Maryland College Park, College Park, MD 20742, USA
Fund Project:Project supported by the National Natural Science Foundation of China (Grant No. 50877023) and the Specialized Research Fund for the Doctoral Program of Higher Education, China (Grant No. 20121317110002).Received Date:04 September 2015
Accepted Date:10 November 2015
Published Online:05 February 2016
Abstract: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 (ECoG) 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 speech-related 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 ECoG 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-ECoG 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 (TV-DBN). 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.
Keywords: ECoG/
high gamma/
time-varying dynamic bayesian networks
