Temperodynamic neural variability as an early-emerging biomarker of autism spectrum disorders

温度动力学神经变异作为自闭症谱系障碍的早期生物标志物

• 批准号: 10687865
• 负责人: Meghan H Puglia
• 金额: $ 17.19万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10687865
• 关键词: Adolescent; Adult; Age; Age Months; Algorithms; Architecture; Behavior; Behavioral; Behavioral Symptoms; Biological Markers; Brain; Caliber; Child; Child Health; Classification; Clinical; Clinical Trials; Collaborations; Coupling; DNA Methylation; Data; Data Science; Data Set; Dedications; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Dimensions; Disease; Early Intervention; Early identification; Electroencephalography; Enrollment; Entropy; Environment; Epigenetic Process; Foundations; Genetic; Goals; Heterogeneity; Individual Differences; Infant; Lead; Life; Link; Longitudinal Studies; Measures; Mentors; Modeling; Nature; Neurobiology; Neurocognitive; Neurosciences; Noise; Outcome; Oxytocin; Oxytocin Receptor; Pathway interactions; Play; Prediction of Response to Therapy; Predictive Analytics; Premature Infant; Receptor Gene; Research; Rest; Risk; Role; Sampling; Sensitivity and Specificity; Series; Signal Transduction; Signs and Symptoms; Social Behavior; Social Perception; Stratification; Testing; Time; Training; Treatment outcome; Universities; Virginia; Work; aged; autism spectrum disorder; autistic; autistic children; behavior observation; biomarker development; biotypes; career; cognitive ability; cognitive development; cognitive neuroscience; cognitive performance; cohort; effective intervention; epigenetic marker; epigenome; improved outcome; individualized medicine; large scale data; longitudinal dataset; neural; neural network; neurodevelopment; neurophysiology; novel marker; precision medicine; programs; public health priorities; response biomarker; sex; social deficits; trait; treatment planning; treatment response

Project Summary Current gold-standard diagnostic techniques for Autism Spectrum Disorder (ASD) rely on behavioral observation, and diagnosis is often not conferred until after age 4. This delay is unfortunate, because early intervention can drastically improve outcomes. Given the rapid and sweeping changes in neural architecture, cognitive ability, and behavioral repertoire that occur within the first year of life, identifying early-emerging neurobiological markers that can predict abnormal neurodevelopment before behavioral symptoms manifest is a public health priority. Following the onset of behavioral signs and symptoms, there is great need for stratification biomarkers that can dissect ASD heterogeneity, thereby informing individualized treatment plans. Such a precision-medicine approach necessitates greater understanding of the longitudinal pathways of development in domains. This will set the stage for biomarkers that are sensitive to, and predictive of, changes in behavior resulting from effective interventions. This proposal aims to identify and validate features of temperodynamic brain signal variability that can serve as diagnostic, risk, and/or treatment response biomarkers of social dysfunction in ASD. Traditionally modeled out of analyses as mere “noise”, measures of temperodynamic neural variability capture the inherently fluctuating nature of the brain, which is increasingly understood to play a valuable functional role in the establishment of neural networks and in the transfer of information throughout the brain. Temperodynamic neural variability has been linked to cognitive performance, development, and autism. The current proposal will capitalize and expand upon this promising neurobiological marker to 1) identify and optimize metrics for assessing temperodynamic neural variability by conducting the most comprehensive comparison of time-series analytics of any study to date, and 2) establish these metrics as biomarkers for neurodevelopmental outcomes by leveraging large clinical and longitudinal data sets consisting of multilevel genetic, neural, and behavioral data. The proposed research extends the candidate’s prior work through new training in autism research, translational developmental cognitive neuroscience, and timeseries and predictive analytics applied to large- scale datasets necessary to advance biomarker development. These training goals will support the candidate’s ultimate career goal of developing an independent research program dedicated to the use of interdisciplinary, multidimensional, collaborative, and cutting-edge approaches to understanding the neurobiological and developmental factors that contribute to individual differences in social behavior across the full continuum of abilities – from healthy to disordered. The University of Virginia is committed to high caliber, collaborative research and scientific preeminence in neuroscience, autism, and data science and will provide the ideal environment for conducting this type of interdisciplinary, multidimensional child health-oriented project.

项目摘要 目前自闭症谱系障碍（ASD）的黄金标准诊断技术依赖于行为 观察和诊断通常要到4岁以后才能进行。这种延迟是不幸的，因为早 干预可以大大改善结果。考虑到神经结构的快速和全面变化， 认知能力，和行为剧目发生在生命的第一年，识别早期出现的 神经生物学标记物可以在行为症状出现之前预测异常神经发育， 公共卫生优先。随着行为体征和症状的出现，非常需要分层 生物标志物可以剖析ASD异质性，从而为个性化治疗计划提供信息。这样的 精确医学方法需要更好地理解纵向发展途径 in domains域.这将为对行为变化敏感和预测的生物标志物奠定基础 通过有效的干预。 该提案旨在识别和验证可以服务于 作为ASD中社会功能障碍的诊断、风险和/或治疗反应生物标志物。传统模式 在分析仅仅是“噪音”的情况下，时间动力学神经变异性的测量捕获了内在的波动， 大脑的性质，这是越来越多的理解发挥了宝贵的功能作用，在建立 神经网络和整个大脑的信息传递。温度动力学神经变异性 与认知能力发展和自闭症有关 目前的建议将利用和扩展这种有前途的神经生物学标记物，以1）识别和 优化评估temperodynamic神经变异性的指标， 比较迄今为止任何研究的时间序列分析，以及2）将这些指标作为生物标志物， 神经发育的结果，利用大型临床和纵向数据集组成的多层次 遗传神经和行为数据 拟议中的研究通过自闭症研究的新培训扩展了候选人先前的工作， 翻译发展认知神经科学，时间序列和预测分析应用于大型 扩展数据集，以促进生物标志物的发展。这些培训目标将支持候选人的 最终的职业目标是开发一个独立的研究计划，致力于跨学科的使用， 多维，协作和尖端的方法来理解神经生物学和 发展因素，有助于在整个社会行为的完整连续体的个体差异， 能力-从健康到无序。弗吉尼亚大学致力于高素质，协作 在神经科学，自闭症和数据科学方面的研究和科学优势，并将提供理想的 这是开展这类跨学科、多层面儿童健康项目的良好环境。