脑电通道间因果关系识别发展性阅读障碍的源/汇相连接模式。

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
I Rodríguez-Rodríguez, A Ortiz, N J Gallego-Molina, M A Formoso, W L Woo
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引用次数: 1

摘要

虽然大脑连接网络可以为发展性阅读障碍的理解和诊断提供信息,但其因果关系尚未得到充分的研究。利用脑电图信号和4.8 Hz(韵律-音节频率)的带限白噪声刺激,我们测量了通道之间的相位格兰杰因果关系,以识别阅读困难学习者和对照组之间的差异,从而提出了一种计算方向连通性的方法。由于因果关系在两个方向上运行,我们探讨了三种情况,即渠道的活动作为来源,作为汇,和总的。我们提出的方法可以用于分类和探索性分析。在所有情况下,我们发现证实了建立的右偏侧Theta采样网络异常,符合Theta和Gamma波段振荡差异的时间采样框架的假设。此外,我们表明,这种异常主要发生在作为汇的通道的因果关系中,在这种关系中,它比只观察总活动时更为明显。在汇场景中,我们的分类器在Theta和Gamma波段分别获得0.84和0.88精度和0.87和0.93 AUC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EEG Interchannel Causality to Identify Source/Sink Phase Connectivity Patterns in Developmental Dyslexia.

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the assumption of the temporal sampling framework of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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