EEG Complexity Trajectory as an Early Biomarker for Autism

描述(由申请人提供)：作为自闭症谱系障碍生物标记物的脑电复杂性儿童医院信息学计划(CHIP)讲师William Bosl(PI)哈佛大学教授和波士顿大学心理学系发展医学系教授Charles Nelson(合作者)波士顿大学心理学系海伦·塔格-弗卢斯伯格教授摘要自闭症谱系障碍(ASD)是复杂的，可遗传的疾病，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的区域和不同的范围内有所不同[1]。在自闭症患者的大脑中，由于突触修剪或形成的问题，高局部连通性和低远程连通性可能同时发展[1，2]。对神经连通性变化的估计可能是导致ASD行为的异常连通性发育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计算特征，以表征复杂系统(如大脑)的动力学。我们的假设是，多尺度熵(MSE)是信号复杂性的一种衡量标准，它将揭示正常发育的大脑和那些最终将被诊断为ASD的大脑之间明显、可测量的差异。机器学习算法将使用MSE值将婴儿分为三组之一：对照组(CON)，基于有患有自闭症的哥哥姐姐的高风险，但不患自闭症(HRN)和高风险并发展为自闭症(HRA)。MSE值不会映射到这三个风险组，而是会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同等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，正常对照组和一组基于家族史的ASD高危婴儿之间存在明显差异(Bosl等人，2011年)。这一变化在9至12个月大的正常婴儿中尤其显著，此时正常婴儿有望达到关键的认知里程碑。在拟议的研究中，我们还将尝试使用MSE增长轨迹来预测结果，特征向量由指定年龄以下的所有值组成：6-9、6-12、6-18和6-24个月。我们假设，在一个给定的年龄，增长轨迹可能比衡量标准更具信息性。为了建立基于MSE的预测比较的基线，将使用机器学习算法进行类似的预测计算，使用所有可用的评估分数，包括ADOS、SCQ、Mullen和头围等生理指标。即使18个月前一岁的评估分数不可能诊断出自闭症，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大(无论是脑电通道还是评估分数)。最后，将确定MSE值和评估分数之间的相关性，以判断MSE测量是评估分数的替代，还是提供补充信息，或者两者都不是。众所周知，早期诊断和治疗可以显著改善ASD患者的长期预后。该项目有可能在生命的第一年内对ASD进行早期诊断，并评估其严重性。如果成功，这也将使旨在避免自闭症大脑功能倾向在完全形成之前发展的一类新疗法成为可能。这里开发的新方法，使用复杂系统方法提取信号特征，并使用机器学习将MSE值、传统评分和身体测量映射到自闭症严重程度估计，可能会广泛应用于识别其他精神疾病的定量生物标志物。这在资源匮乏的地区可能特别有价值，因为那里几乎没有专业人员来完成行为评估。PHS 398/2590(11/07版)页面续格式页面

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