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首页> 外文期刊>Journal of integrative neuroscience. >Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks
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Shortest path based network analysis to characterize cognitive load states of human brain using EEG based functional brain networks

机译：基于脑电站的功能性脑网络的最短路径的网络分析来表征人脑的认知负荷状态

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Understanding and analyzing the dynamic interactions among millions of spatially distributed and functionally connected regions in the human brain constituting a massively parallel communication system is one of the major challenges in computational neuroscience. Many studies in the recent past have employed graph theory to efficiently model, quantitatively analyze, and understand the brain's electrical activity. Since, the human brain is believed to broadcast information with reduced material and metabolic costs, identifying various brain regions in the shortest pathways of information dissemination becomes essential to understand the intricacies of brain function. This paper proposes a graph theoretic approach using the concept of shortest communication paths between various brain regions (electrode sites) to identify the significant communication pathways of information exchange between various nodes in the functional brain networks constructed from multi-channel electroencephalograph data. A special weighted network called the Shortest Path Network is constructed from a functional brain network where the edge weight is computed as the sum of frequency of occurrence of that edge in all possible shortest paths between every pair of electrodes. The weighted Shortest Path Networks thus constructed portray information on the number of times the edges are used in information propagation during different cognitive states. This network is further analyzed by computing the influential communication paths to characterize the information dissemination among brain regions during different cognitive load conditions. The experimental results presented demonstrate the efficacy of this novel graph theoretic approach in identifying, quantifying, and comparing the significant shortest pathways of information exchange during mild and heavy cognitive load conditions. Analysis also suggests that future research should consider the biological inferences of the ability of the human brain to use reduced material and metabolic cost during the instantaneous transmission of information.
机译：理解和分析在构成大规模平行通信系统的人脑中数百万空间分布和功能连接区域之间的动态相互作用是计算神经科学中的主要挑战之一。最近过去的许多研究都使用了图表理论，以有效地模拟，定量分析，并理解大脑的电力活动。由于，人大脑被认为以降低的材料和代谢成本广播信息，识别信息传播最短通路中的各种脑区变得必不可了解脑功能的复杂性必不可少。本文提出了一种利用各种脑区（电极站点）之间的最短通信路径概念的图形理论方法，以识别由多通道脑电图数据构成的功能脑网络中的各种节点之间的信息交换的显着通信路径。称为最短路径网络的特殊加权网络由功能大脑网络构成，其中边缘重量被计算为在每对电极之间的所有可能的最短路径中的该边缘的发生频率之和。因此，加权最短路径网络构造了关于边缘在不同认知状态期间的信息传播中使用的次数的描绘信息。通过计算有影响力的通信路径来进一步分析该网络，以在不同的认知载荷条件下表征脑区域的信息传播。提出的实验结果证明了这种新颖的曲线理论方法在识别，量化和比较了轻度和重认知载荷条件期间信息交换的显着最短路径的功效。分析还表明，未来的研究应考虑人类大脑在瞬时传播期间使用降低材料和代谢成本的能力的生物推迟。

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