Harvard Medical School, Boston, MA, USA.
BMC Med. 2011 Feb 22;9:18. doi: 10.1186/1741-7015-9-18.
Complex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD.
Using mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months.
Multiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA) than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter.
This proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is the first demonstration of an information theoretic analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder.
复杂的神经发育障碍可能具有细微的脑功能特征，这些特征在行为症状明显之前很早就出现了。这种内表型可能是以后认知障碍的可测量生物标志物。脑电图（EEG）信号的非线性复杂性被认为包含了大脑中神经网络结构的许多尺度上的信息。EEG 信号异常的早期检测可能是发育性认知障碍的早期生物标志物。本文的目的是证明基于静息状态 EEG 数据计算的修正多尺度熵（mMSE）可以用作正常大脑发育的生物标志物，并将典型的发育儿童与一组自闭症谱系障碍（ASD）高风险的婴儿区分开来，这是基于 ASD 先证者定义的。
使用 mMSE 作为特征向量，使用多类支持向量机算法对典型发育组和高风险组进行分类。在 6 至 24 个月的每个年龄组内分别计算分类。
多尺度熵在自闭症高风险婴儿（HRA）中的发展轨迹似乎与典型发育对照组不同。差异似乎在 9 至 12 个月时最大。使用几种机器学习算法，将 mMSE 作为特征向量，9 个月大的婴儿以超过 80%的准确率被分类为对照组和 HRA 组。9 个月大的男孩分类准确率接近 100%，12 个月和 18 个月时仍然很高（70%至 90%）。对于女孩，分类准确率在 6 个月时最高，但此后下降。
这项原理验证研究表明，从静息状态 EEG 信号计算出的 mMSE 可能是检测 ASD 风险和婴儿认知发育异常的有用生物标志物。据我们所知，这是首次对具有复杂神经发育障碍风险的婴儿 EEG 数据进行信息论分析以寻找生物标志物的研究。
