Age Effect in Human Brain Responses to Emotion Arousing Images: The EEG 3D-Vector Field Tomography Modeling Approach

Chrysa D. Papadaniil, V. Kosmidou, A. Tsolaki, L. Hadjileontiadis, M. Tsolaki, Y. Kompatsiaris
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引用次数: 6

Abstract

Understanding of the brain responses to emotional stimulation remains a great challenge. Studies on the aging effect in neural activation report controversial results. In this paper, pictures of two classes of facial affect, i.e., anger and fear, were presented to young and elderly participants. High-density 256-channel EEG data were recorded and an innovative methodology was used to map the activated brain state at the N170 event-related potential component. The methodology, namely 3D Vector Field Tomography, reconstructs the electrostatic field within the head volume and requires no prior modeling of the individual's brain. Results showed that the elderly exhibited greater N170 amplitudes, while age-based differences were also observed in the topographic distribution of the EEG recordings at the N170 component. The brain activation analysis was performed by adopting a set of regions of interest. Results on the maximum activation area appeared to be emotion-specific; the anger emotional conditions induced the maximum activation in the inferior frontal gyrus, while fear activated more the superior temporal gyrus. The approach used here shows the potential of the proposed computational model to reveal the age effect on the brain activation upon emotion arousing images, which could be further transferred to the design of assistive clinical applications.
人类大脑对情绪激发图像反应的年龄效应:脑电图三维矢量场断层成像建模方法
理解大脑对情绪刺激的反应仍然是一个巨大的挑战。关于神经激活中的衰老效应的研究报告了有争议的结果。在本文中,两类面部情绪的图片,即愤怒和恐惧,呈现给年轻和老年参与者。记录高密度的256通道脑电图数据,并采用创新的方法绘制N170事件相关电位分量的激活脑状态。该方法,即三维矢量场断层扫描,重建头部体积内的静电场,不需要事先对个体大脑进行建模。结果表明,老年人的N170振幅更大,N170分量的脑电记录的地形分布也存在年龄差异。大脑激活分析是通过采用一组感兴趣的区域进行的。最大激活区域的结果似乎与情绪有关;愤怒情绪诱发额下回的最大激活,而恐惧情绪诱发颞上回的最大激活。本文采用的方法显示了所提出的计算模型在揭示情绪激发图像对大脑激活的年龄影响方面的潜力,这可以进一步转移到辅助临床应用的设计中。
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来源期刊
IEEE Transactions on Autonomous Mental Development
IEEE Transactions on Autonomous Mental Development COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS
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