EEG Complexity Trajectory as an Early Biomarker for Autism

描述（申请人提供）：EEG复杂性作为自闭症谱系障碍风险人员的生物标志物William Bosl（PI）儿童医院信息学项目（ChIP）讲师Charles纳尔逊（合作者）教授，哈佛和CHB发展医学部Helen Tager-Flusberg（顾问）波士顿大学心理学系教授摘要自闭症谱系障碍（ASD）是复杂的，遗传性疾病，具有高度可变的长期结果。研究表明，自闭症等复杂的精神障碍与异常的大脑连接有关，这种连接可能在不同区域和不同尺度之间存在差异[1]。在自闭症患者的大脑中，由于突触修剪或形成的问题，高局部连接性和低远程连接性可能会同时发展[1，2]。估计神经连接的变化可能是导致ASD行为的异常连接发展的有效诊断生物标志物。神经网络产生的电信号包含有关网络结构的信息[3-5]。非线性信号处理算法[6，7]可用于从网络产生的时间序列中计算特征，以表征复杂系统（如大脑）的动态。我们的假设是，多尺度熵（MSE）是信号复杂性的一种度量，它将揭示正常发育的大脑和最终被诊断为ASD的大脑之间明显的、可测量的差异。机器学习算法将用于使用MSE值将婴儿分为三组之一：对照组（CON），基于有自闭症的哥哥姐姐的高风险，但不会发展自闭症（HRN）和高风险并发展自闭症（HRA）。而不是映射到这三个风险组，MSE值将映射到基于自闭症诊断观察计划（ADOS）或对研究中所有婴儿进行的等效评估的严重程度评分。使用MSE测量信号复杂性的初步数据显示了正常对照和基于家族史的发展ASD的高风险婴儿组之间的明显差异（Bosl等人，2011年）。这种变化在9至12个月大的婴儿中尤为显著，因为正常婴儿预计会出现关键的认知里程碑。在拟议的研究中，我们还将尝试使用MSE增长轨迹预测结果，特征向量由指定年龄的所有值组成：6-9，6-12，6-18和6-24个月。我们假设，在一个给定的年龄，增长轨迹可能比措施更有信息。为了建立用于比较基于MSE的预测的基线，将使用机器学习算法对所有可用的评估评分进行类似的预测计算，包括ADOS、SCQ、马伦和生理测量，如头围。即使不能从18个月前的评估分数中诊断出自闭症，评估分数的轨迹也可能具有预测价值。在所有预测计算中，将估计哪些变量对预测贡献最多的信息（无论是EEG通道还是评估评分）。最后，将确定MSE值和评估分数之间的相关性，以判断MSE测量值是否是评估分数的代理，或提供补充信息，或两者都不是。已知早期诊断和治疗可显著改善ASD患者的长期预后。该项目有可能在生命的第一年内早期诊断ASD，并评估严重程度。如果成功的话，这也将成为一种新的治疗方法，旨在避免自闭症大脑功能倾向在完全形成之前的发展。这里开发的新方法，使用复杂的系统方法来提取信号特征和机器学习来映射MSE值，传统分数和物理测量到自闭症严重程度估计，可能广泛适用于识别其他精神障碍的定量生物标志物的方法。这在资源贫乏的地区可能具有特别的价值，因为那里很少有专业人员可以进行完整的行为评估。PHS 398/2590（Rev. 11/07）

DESCRIPTION (provided by applicant): EEG Complexity as a Biomarker for Autism Spectrum Disorder Risk Personnel William Bosl (PI) Instructor, Children's Hospital Informatics Program (ChIP) Charles Nelson (collaborator) Professor, Harvard and Division of Developmental Medicine at CHB Helen Tager-Flusberg (consultant) Professor, Boston University Department of Psychology Abstract Autism spectrum disorders (ASD) are complex, heritable disorders that have highly variable long-term outcomes. Research suggests that complex mental disorders such as autism are associated with abnormal brain connectivity that may vary between different regions and different scales [1]. In the autistic brain, high local connectivity and low long-range connectivity may develop concurrently due to problems with synapse pruning or formation [1, 2]. Estimation of changes in neural connectivity might be an effective diagnostic biomarker for abnormal connectivity development that leads to ASD behaviors. The electrical signals produced by neural networks contain information about the network structure [3-5]. Nonlinear signal processing algorithms [6, 7] can be used to compute features from the time series produced by the network to characterize the dynamics of a complex system such as the brain. Our hypothesis is that multiscale entropy (MSE) is one measure of signal complexity that will reveal distinct, measurable differences between normally developing brains and those that will eventually be diagnosed with an ASD. Machine learning algorithms will be used to classify infants into one of three groups using MSE values: controls (CON), high risk based on having an older sibling with autism, but does not develop autism (HRN) and high risk and develops autism (HRA). Rather than mapping to these three risk groups, MSE values will be mapped to a severity score based on Autism Diagnostic Observation Schedule (ADOS) or equivalent assessments given to all infants in the study. Preliminary data using MSE to measure signal complexity shows a clear difference between normal controls and a group of infants at high risk for developing ASD based on family history (Bosl, et al., 2011). The change was particularly striking between 9 and 12 months of age when critical cognitive milestones are expected in normal infants. In the proposed study, we will also attempt to make predictions of outcome using MSE growth trajectories, with feature vectors composed of all values up to a specified age: 6-9, 6-12, 6-18 and 6-24 months. We hypothesize that the growth trajectories may be more informative than measures at one given age. To establish a baseline for comparison of predictions based on MSE, a similar prediction calculation using machine learning algorithms will be done with all available assessment scores, including ADOS, SCQ, Mullen and physiological measures such as head circumference. Even if a diagnosis of autism is not possible from assessment scores at one age before 18 months, it may be that the trajectory of assessment scores has predictive value. In all prediction computations, an estimate will be made of which variables contribute the most information to the prediction (whether EEG channels or assessment scores). Finally, correlations between MSE values and assessment scores will be determined in order to judge whether MSE measurements are proxies for assessment scores, or contribute complementary information, or neither. Early diagnosis and therapy are known to significantly improve the long-term prognosis of ASD patients. This project has the potential to enable early diagnosis of ASD, within the first year of life, and assess severity. If successful, this will also enable a new class of therapies aimed at averting the development of autistic brain functional tendencies before they are fully formed. The novel methodology developed here, using complex systems methods to extract signal features and machine learning to map MSE values, traditional scores and physical measurements to autism severity estimates, may be widely applicable as an approach for identifying quantitative biomarkers of other mental disorders. This may have particular value in resource poor regions where few professionals are available for complete behavioral assessments. PHS 398/2590 (Rev. 11/07) Page Continuation Format Page