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The research of constructing dynamic cognition model based on brain network

机译:基于脑网络的动态认知模型构建研究

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摘要

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.
机译:估计大脑区域与认知,行为和精神病学领域中相应过程之间的功能相互作用和联系是理解人类连接组的主要追求。很少有研究使用头皮脑电图(EEG)数据中的脑网络模型检查动态进化对认知过程和大脑激活的影响。这项研究的目的是调查脑功能连通性,并根据EEG数据构建动态编程模型,并评估脑连通性的拓扑特征与认知进化过程之间的可能关联。在这里,大脑区域之间的功能连通性被定义为不同大脑区域中EEG信号之间的统计依赖性,通常通过使用小波相干性计算区域时间序列之间的关系来确定。我们提出了一种构建动态认知模型的加速动态编程算法,该算法发现空间分布区域的相干连接差异,拓扑特征可以用来传递信息,产生临时网络状态。我们的发现表明,在变化的音频刺激之后,大脑动力学会随着时间的推移而引起复杂网络属性的变化,动态编程模型会在不同的时间和频率下进行动态演化处理。本文采用一种新的构造方法来了解整个大脑网络的动力学,首先,通过小波相干来构造大脑网络,其次,通过网络拓扑特征和最小生成树选择不同时间活跃的大脑区域。最后,通过动态编程算法构建了动态进化模型,以理解认知过程,并将该模型应用于听觉实验,结果表明,在变声刺激后,从数量上观察到更多的相关性,脑电功能连接动态进化模型对认知过程的影响小波相干脑电图记录是可行的。

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