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Cognitive Load Driven Directed Information Flow in Functional Brain Networks

机译：认知负载驱动的功能脑网络中的指示信息流

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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：人脑连接分析描述了结构和功能性脑网络的模式，近年来已成为计算神经科学中最受研究的主题之一。基于信息流向的功能性大脑网络的详细研究随后获得了意义。该研究识别与使用从脑电图（EEG）信号估计的标准化传输熵（NTE）相比，与基线状态相比，在认知活动期间的信息流方向的变化。提出了一种算法，用于在大脑的各个区域（LOBES）中找到认知状态特定信息流方向模式（IFDP）。结果清楚地表明，基于IFDP的分析能够在四种不同脑区的认知活动期间检测变化的信息流定向模式：正面，中央，顶叶和枕骨。在认知活动期间，从前部到中央，顶叶和枕叶的定向功能脑网络中建立明显的长距离互连，以及从枕骨叶中的中心。这表明IFDP方法可能具有在检测认知障碍以及临床研究中的潜在应用，例如，在癫痫中寻找癫痫发作症。

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