EEG Complexity Trajectory as an Early Biomarker for Autism

脑电图复杂性轨迹作为自闭症的早期生物标志物

• 批准号: 8410558
• 负责人: WILLIAM J BOSL
• 金额: $ 20.88万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8410558
• 关键词: Affect; Age; Age-Months; Algorithms; Autistic Disorder; Behavior; Behavior assessment; Behavioral; Biological Markers; Biological Neural Networks; Boston; Brain; Characteristics; Child; Classification; Code; Cognitive; Communication; Complex; Data; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Disease; Early Diagnosis; Early Intervention; Early treatment; Effectiveness; Electroencephalography; Employee Strikes; Entropy; Family; Functional disorder; Funding; Future; Goals; Grant; Growth; Handedness; Head circumference; Human Resources; Impairment; Infant; Informatics; Intelligence Tests; Language; Left; Life; Longitudinal Studies; Machine Learning; Maps; Measurable; Measurement; Measures; Medicine; Mental disorders; Methodology; Methods; Monitor; Neurodevelopmental Disorder; Outcome; Patients; Pattern; Pediatric Hospitals; Physiological; Positioning Attribute; Predictive Value; Proxy; Psychology; Recording of previous events; Research; Resources; Rest; Risk; Schedule; Series; Severities; Siblings; Signal Transduction; Social Interaction; Specific qualifier value; Statistical Methods; Structure; Synapses; Syndrome; System; Testing; Time; Triad Acrylic Resin; United States National Institutes of Health; Universities; abstracting; autism spectrum disorder; base; behavior measurement; computerized data processing; disorder risk; endophenotype; high risk; improved; instructor; interest; meetings; neurophysiology; novel; novel diagnostics; outcome forecast; professor; programs; relating to nervous system; skills; social; vector

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

描述（由申请人提供）：脑电图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）讲师，儿童医院信息学项目（ChIP） Charles Nelson（合作者）教授，哈佛大学和CHB发育医学部教授Helen Tager-Flusberg（顾问）教授，波士顿大学心理学系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预后。研究表明，复杂的精神障碍，如自闭症，与大脑连接异常有关，这种异常可能在不同的区域和不同的尺度上有所不同。在自闭症大脑中，由于突触修剪或形成问题，高局部连通性和低远程连通性可能同时发生[1,2]。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了网络结构的信息[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测量方法，它将揭示正常发育的大脑与最终被诊断为自闭症的大脑之间明显的、可测量的差异。机器学习算法将使用MSE值将婴儿分为三组：对照组（CON），高风险的基础上有一个患有自闭症的兄弟姐妹，但不会发展为自闭症（HRN）和高风险并发展为自闭症（HRA）。MSE值不是映射到这三个风险组，而是映射到基于自闭症诊断观察表（ADOS）或对研究中所有婴儿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，正常对照和基于家族史的ASD高风险婴儿组之间存在明显差异（Bosl等，2011）。这种变化在9到12个月之间尤为显著，而这段时间是正常婴儿预期的关键认知里程碑。在拟议的研究中，我们还将尝试使用MSE生长轨迹来预测结果，特征向量由特定年龄的所有值组成：6-9个月，6-12个月，6-18个月和6-24个月。我们假设，生长轨迹可能比一个给定年龄的测量更有信息。为了建立基于MSE的预测比较基线，将使用机器学习算法对所有可用的评估分数进行类似的预测计算，包括ADOS， SCQ， Mullen和生理测量（如头围）。即使不能通过18个月前的评估分数来诊断自闭症，评估分数的轨迹也可能具有预测价值。在所有预测计算中，将对哪些变量对预测贡献最多的信息（无论是EEG通道还是评估分数）进行估计。最后，确定MSE值与评估分数之间的相关性，以判断MSE测量是否代表评估分数，或提供补充信息，或两者都不提供。早期诊断和治疗可以显著改善ASD患者的长期预后。这个项目有可能使自闭症谱系障碍的早期诊断，在生命的第一年，并评估严重程度。如果成功，这也将使一种新的治疗方法成为可能，旨在避免自闭症大脑功能倾向的发展，在它们完全形成之前。本文开发的新方法，使用复杂系统方法提取信号特征，并使用机器学习将MSE值，传统分数和物理测量映射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。小灵通398/2590 （Rev. 11/07）页延续格式页