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

项目摘要 目前自闭症谱系障碍(ASD)的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，因为早期 干预可以极大地改善结果。鉴于神经结构的快速而彻底的变化， 认知能力和行为能力，发生在生命的第一年，识别早熟 可以在行为症状显现之前预测神经发育异常的神经生物学标记物是 这是公共卫生的首要任务。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的异质性，从而为个性化治疗计划提供信息。这样的一个 精准医学方法需要更多地了解发展的纵向路径 在域中。这将为对行为变化敏感并可预测的生物标志物奠定基础 这是有效干预的结果。 这项建议旨在识别和验证温度动力学脑信号变异性的特征， 作为自闭症患者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 出于单纯的“噪声”分析，温度动力学神经可变性的测量捕捉到了内在的波动。 大脑的性质，越来越多地被理解为在建立 神经网络和整个大脑中的信息传输。温度动力学神经变异性 与认知表现、发育和自闭症有关。 目前的提议将利用并扩展这一前景光明的神经生物标记来1)识别和 优化用于评估温度动力学神经变异性的指标，通过进行最全面的 对迄今为止任何研究的时间序列分析进行比较，以及2)将这些指标确定为 利用由多个水平组成的大型临床和纵向数据集的神经发育结果 遗传、神经和行为数据。 这项拟议的研究通过在自闭症研究方面的新培训来扩展候选人之前的工作， 翻译发展认知神经科学，时间序列和预测分析应用于大型- 扩大推进生物标记物开发所需的数据集。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项目，致力于使用跨学科， 多维、协作和前沿的方法来了解神经生物学和 在整个社会统一体中导致个体社会行为差异的发展因素 能力--从健康到紊乱。弗吉尼亚大学致力于高水平、协作性 在神经科学、自闭症和数据科学方面的研究和科学成就卓越，并将提供理想的 为开展这类跨学科、多层面的面向儿童健康的项目创造了环境。

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.