Meyer-Lindenberg A
Centre for Psychiatry, Justus-Lichtz-University Medical School, Giessen, Germany.
Electroencephalogr Clin Neurophysiol. 1996 Nov;99(5):405-11. doi: 10.1016/s0013-4694(96)95699-0.
Analysis of the EEG as a signal from a deterministic non-linear system should, in principle, allow insights into the complexity of underlying brain activity. We examined the capability of this method to analyse the marked changes in brain activity during normal brain development. Resting EEGs of 54 healthy children (newborns to 14 years old) and of 12 normal adults were recorded digitally. The following parameters were calculated: correlation dimension, a measure of the complexity of the underlying system, and the first Lyapunov coefficient, indicating the system's 'unpredictability'. Analysis of variance (ANOVA) was performed with probands grouped by age. The subgroups of children older than 1 year was further examined by regression analysis. In all analysed epochs, Lyapunov coefficients were significantly positive (P < 0.0001. t-test). The presence of non-linear dynamics was asserted statistically in 64-76% of examined epochs. A highly significant increase in correlation dimension with age was found in all examined leads (P < 0.0001, ANOVA). In all age groups, marked differences in correlation dimension in different brain regions became evident (P < 0.01-0.0001, ANOVA). Evidence for the presence of non-linearity can be found even in newborns. Brain maturation was reflected in a marked and highly significant increase in correlation dimension (complexity). Our work indicates that non-linear dynamics analysis is suitable for measuring complexity of brain activity during maturation and provides age-dependent normal values as a basis for further study.
从确定性非线性系统的角度对脑电图信号进行分析，原则上应有助于洞察潜在大脑活动的复杂性。我们研究了该方法分析正常大脑发育过程中大脑活动显著变化的能力。对54名健康儿童（从新生儿到14岁）和12名正常成年人的静息脑电图进行了数字记录。计算了以下参数：关联维数，这是衡量潜在系统复杂性的一个指标；以及第一李雅普诺夫系数，它表明系统的“不可预测性”。按年龄对受试者进行分组后进行方差分析（ANOVA）。对1岁以上儿童的亚组进一步进行回归分析。在所有分析的时段中，李雅普诺夫系数均显著为正（P<0.0001，t检验）。在所检查时段的64%-76%中，经统计学验证存在非线性动力学。在所有检查导联中均发现关联维数随年龄显著增加（P<0.0001，ANOVA）。在所有年龄组中，不同脑区的关联维数存在显著差异（P<0.01-0.0001，ANOVA）。即使在新生儿中也能发现非线性存在的证据。大脑成熟表现为关联维数（复杂性）显著且高度显著增加。我们的研究表明，非线性动力学分析适用于测量大脑成熟过程中大脑活动的复杂性，并提供与年龄相关的正常值作为进一步研究的基础。
