Thatcher R W, North D, Biver C
EEG and NeuroImaging Laboratory, Bay Pines VA Medical Center, Research and Development Service-151, 10000 Bay Pines Blvd., Bldge 23, Room 117, Petersburg, Bay Pines, FL 33744, USA.
Clin Neurophysiol. 2005 Sep;116(9):2129-41. doi: 10.1016/j.clinph.2005.04.026.
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).
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.
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.
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.
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.
脑电图测量有两个相互关联的类别：1. 脑电电流或功率；2. 脑电网络特性，如相干性和相位延迟。本研究的目的是比较这两种不同类别的脑电图测量方法预测韦氏智力测验（WISC - R）成绩的能力。
对442名年龄在5至52岁的受试者，采用双耳链接参考，从19个头皮部位记录闭眼静息脑电图。对同一批受试者进行韦氏智力测验，但不在记录脑电图时进行。受试者被分为高智商（≥120）和低智商（≤90）组。将P<0.05时的脑电图变量进行因子分析，然后将每个因子上载荷最高的单个变量纳入判别分析，分组为高智商组与低智商组。
高智商与低智商的判别分析准确率为92.81 - 97.14%。中等智商受试者（即90 <智商< 120）的判别分数介于高智商组和低智商组之间。智商的线性回归预测与判别分数显著相关（r = 0.818 - 0.825，P < 10(-6)）。效应大小的排序为脑电相位>脑电相干性>脑电幅度不对称性>绝对功率>相对功率和功率比。与智商相关性最强的是额叶短脑电相位延迟和后皮质区域长相位延迟、相干性降低和绝对功率增加。
研究结果与神经效率提高和大脑复杂性增加与智力呈正相关一致，且神经资源的额叶同步是脑电与智力相关性的重要促成因素。
脑电图对智力的定量预测提供了对认知功能中等至较强的效应大小估计，同时揭示了对智力神经生理基础的更深入理解。
