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EEG and intelligence: relations between EEG coherence, EEG phase delay and power.

机译:脑电和智力:脑电连贯性,脑电相位延迟和功率之间的关系。

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OBJECTIVE: There are two inter-related categories of EEG measurement: 1, EEG currents or power and; 2, EEG network properties such as coherence and phase delays. The purpose of this study was to compare the ability of these two different categories of EEG measurement to predict performance on the Weschler Intelligence test (WISC-R). METHODS: Resting eyes closed EEG was recorded from 19 scalp locations with a linked ears reference from 442 subjects aged 5-52 years. The Weschler Intelligence test was administered to the same subjects but not while the EEG was recorded. Subjects were divided into high IQ (> or = 120) and low IQ (< or = 90) groups. EEG variables at P<.05 were entered into a factor analysis and then the single highest loading variable on each factor was entered into a discriminant analysis where groups were high IQ vs. low.Q. RESULTS: Discriminant analysis of high vs. low IQ was 92.81-97.14% accurate. Discriminant scores of intermediate IQ subjects (i.e. 90 < IQ < 120) were intermediate between the high and low IQ groups. Linear regression predictions of IQ significantly correlated with the discriminant scores (r = 0.818-0.825, P < 10(-6)). The ranking of effect size was EEG phase > EEG coherence > EEG amplitude asymmetry > absolute power > relative power and power ratios. The strongest correlations to IQ were short EEG phase delays in the frontal lobes and long phase delays in the posterior cortical regions, reduced coherence and increased absolute power. CONCLUSIONS: The findings are consistent with increased neural efficiency and increased brain complexity as positively related to intelligence, and with frontal lobe synchronization of neural resources as a significant contributing factor to EEG and intelligence correlations. SIGNIFICANCE: Quantitative EEG predictions of intelligence provide medium to strong effect size estimates of cognitive functioning while simultaneously revealing a deeper understanding of the neurophysiological substrates of intelligence.
机译:目标:脑电测量有两个相互关联的类别:1,脑电电流或功率;以及2,脑电网络的属性,如相干性和相位延迟。这项研究的目的是比较这两种不同类别的EEG测量在Weschler Intelligence测试(WISC-R)上预测性能的能力。方法:从19个头皮位置记录静息闭眼的EEG,并从442个年龄在5-52岁的受试者的耳朵参考中进行记录。对相同的受试者进行韦氏智力测验,但在记录脑电图时不进行。将受试者分为高智商(>或= 120)和低智商(<或= 90)组。将P <.05的EEG变量输入到因子分析中,然后将每个因子上的单个最高负荷变量输入到判别分析中,在这些分析中,组的IQ值高而Q值低。结果:高智商与低智商的判别分析的准确度为92.81-97.14%。中智商的判别分数(即90 <智商<120)在智商高和智商低之间。智商的线性回归预测与判别分数显着相关(r = 0.818-0.825,P <10(-6))。效应大小的等级是脑电相位>脑电相干性>脑电振幅不对称>绝对功率>相对功率和功率比。与智商最强的相关性是额叶中的EEG相位延迟短,后皮质区域中的相位延迟长,相干性降低,绝对功率增加。结论:这些发现与增加的神经效率和增加的脑部复杂性(与智力呈正相关)相一致,并且与额叶神经资源的同步是脑电图和智力相关性的重要因素。意义:智力的定量EEG预测提供了对认知功能的中等至强烈影响大小的估计,同时揭示了对智力的神经生理学基础的更深了解。

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