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Cognitive Load Driven Directed Information Flow in Functional Brain Networks

机译：功能脑网络中认知负载驱动的定向信息流

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The human brain connectome analysis describes the patterns of structural and functional brain networks and has become one of the most studied topics in computational neuroscience in recent years. Detailed investigation of functional brain networks based on the direction of information flow has subsequently gained significance. This study identifies changes in information flow direction between different brain regions during cognitive activity compared to baseline state using Normalized Transfer Entropy (NTE) estimated from electroencephalogram (EEG) signals. An algorithm is proposed for finding the cognitive state specific information flow direction patterns (IFDP) among various regions (lobes) of the brain. Results clearly demonstrate that IFDP based analysis is able to detect the changing information flow directional patterns during cognitive activity among four different brain regions: Frontal, Central, Parietal and Occipital. During cognitive activity, noticeable long range interconnections are established in the directed functional brain network from frontal to central, parietal and occipital lobes, and as well as from the central to occipital lobe. This suggests that the IFDP approach may have potential applications in the detection of cognitive impairments as well as in the clinical research e.g., for finding seizure foci in epilepsy.
机译：人脑连接体分析描述了结构和功能性脑网络的模式，并且已成为近年来在计算神经科学中研究最多的主题之一。基于信息流方向的功能性大脑网络的详细研究随后具有重要意义。这项研究使用脑电图（EEG）信号估计的归一化转移熵（NTE）来识别与基线状态相比在认知活动过程中不同大脑区域之间信息流方向的变化。提出了一种算法，用于在大脑的各个区域（叶）之间找到特定于认知状态的信息流方向模式（IFDP）。结果清楚地表明，基于IFDP的分析能够在认知活动期间检测到四个不同的大脑区域（额叶，中央，顶叶和枕叶）中不断变化的信息流方向模式。在认知活动期间，在定向功能性大脑网络中，从额叶到中央，顶叶和枕叶以及从中央到枕叶建立了明显的远程互连。这表明IFDP方法可能在认知障碍的检测以及临床研究例如在癫痫中发现癫痫病灶方面具有潜在的应用。

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