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      Power spectrum study of EEG resting-state in students with high trait anxiety

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          Abstract

          Objective To investigats the specific changes of brain neuron oscillation in non-clinical high-trait anxiety college students by recording the resting state EEG of high and low trait anxiety subjects.

          Methods College students in a university were administered by using the S-TA Inventory, 27% of the number of people before and after the selection were divided into low-specific focus group (15) and high-trait anxiety group (15), based on the STAT score. After pre-processing, the data was divided into five frequency bands of δ (1—<4), θ(4 — <8), α(8 — <13), β (13 — <30), γ (30—100) Hz and every electrode power value of those was calculated respectively. Correlation between power spectrum and trait anxiety scores was investigated.

          Results The high- trait anxiety group were in the frontal and central regions ( t = 3.47, 2.62) of the δ band, the frontal region ( t = 2.22) of the θ band, the frontal, central, right temporal, and posterior regions ( t = 2.77, 2.23, 3.65, 2.35) of the β band, the frontal, left temporal, central, right temporal, and posterior regions ( t = 2.83, 2.22, 2.64, 2.43, 2.09) of the γ band, than that in the low trait anxiety group. Furthermore, in central regions of the δ band; the frontal region of the θ band; the frontal, the central, and posterior regions of the β band; the frontal, left temporal, central, and posterior regions ( r = -0.63, -0.51, -0.62, -0.53, -0.54, -0.59, -0.56, -0.55, -0.49) of the γ band, the correlation between trait anxiety scores and the power value were obvious negatively.

          Conclusion High trait anxiety college students have lower power spectrum than low trait anxiety college students. The degree of trait anxiety is related to the power spectrum. The changes of brain resting-state electrical signals in high-trait anxiety individuals may be related to the influence of trait anxiety on college students’ attention and working memory.

          Abstract

          【摘要】 目的 探究不同水平特质焦虑大学生大脑神经元振荡特异性改变, 为对特殊髙校学生群体的临床干预提供 依据。 方法 运用状态-特质焦虑量表中的特质焦虑分量表, 对某髙校 310 名大学生进行测试, 按照被试特质焦虑得分的 分布, 选取前后各 27% 的人划分为低特质焦虑组 (15 名) 和髙特质焦虑组 (15 名) 。经过预处理后将数据划分为 δ(1 ~ < 4), θ(4~<8), α(8~<13), β(13~<30), γ(30~100) Hz 5 个频段, 计算各个电极的功率值, 考察功率谱与特质焦虑得分的 相关性。 结果 髙特质焦虑实验组在 δ 频段的额区、中央区 ( t 值分别为 3.47, 2.62); θ 频段的额区 ( t = 2.22); β 频段的额 区、中央区、右侧颞区、后区 ( t 值分别为 2.77, 2.23, 3.65, 2.35); γ 频段的额区、左侧颞区、中央区、右侧颞区、后区 ( t值分别 为2.83, 2.22, 2.64, 2.43, 2.09)功率低于低特质焦虑实验组 ( P 值均<0.05)。在 δ 频段中央区, θ 频段的额区, β 频段的额区、 中央区、后区, γ 频段的额区、左侧颞区、中央区、后区功率谱值与特质焦虑得分呈负相关 ( r 值分别为-0.63, -0.51, -062, -0.53, -0.54, -0.59, -0.56, -0.55, -0.49, P 值均<0.05)。 结论 髙特质焦虑大学生功率谱较低特质焦虑大学生降低, 特质 焦虑程度与功率谱具有相关性。髙特质焦虑个体在大脑静息状态下脑电信号发生改变可能是与特质焦虑影响大学生注意 力、工作记忆等有关。

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          Author and article information

          Journal
          CJSH
          Chinese Journal of School Health
          Chinese Journal of School Health (China )
          1000-9817
          01 June 2020
          01 June 2020
          : 41
          : 6
          : 882-885
          Affiliations
          [1] 1Nursing College of Xinxiang Medical University, Xinxiang (453003), Henan Province, China
          Author notes
          *Corresponding author: LUO Yanyan, E-mail: 13937315599@ 123456163.com
          Article
          j.cnki.1000-9817.2020.06.022
          10.16835/j.cnki.1000-9817.2020.06.022
          8eba3cb7-8101-4b34-b8e9-623cd9003036
          © 2020 Chinese Journal of School Health

          This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License (CC BY-NC 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See https://creativecommons.org/licenses/by-nc/4.0/.

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          Categories
          Journal Article

          Ophthalmology & Optometry,Pediatrics,Nutrition & Dietetics,Clinical Psychology & Psychiatry,Public health
          Students,Brain,Electroencephalography,Anxiety,Mental health

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