MRI Based Presymptomatic Prediction of ASD

基于 MRI 的 ASD 症状前预测

• 批准号: 9899322
• 负责人: Joseph Piven
• 金额: $ 195.44万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9899322
• 关键词: 2 year old; Affect; Age; Age-Months; Appearance; Area; Behavior; Behavioral; Biological Markers; Brain imaging; Categories; Characteristics; Child; Classification; Clinical; Clinical Trials; Data; Detection; Development; Diagnosis; Diagnostic; Dimensions; Disease; Early Intervention; Enrollment; Family; Funding; Future; Goals; Health Benefit; Impairment; Individual; Infant; Infrastructure; Intervention; Intervention Studies; Language; Life; MRI Scans; Magnetic Resonance Imaging; Measures; Methods; Outcome; Parents; Pharmacology; Population; Predictive Value; Public Health; Publishing; Research Personnel; Rest; Risk; Sample Size; Sampling; Shapes; Siblings; Site; Structure; Surface; Testing; Time; Treatment outcome; Triage; Validation; autism spectrum disorder; autistic; base; clinical predictors; clinical research site; clinically relevant; cohort; cost effective; high risk; high risk infant; infancy; joint attention; neuroimaging; predictive test; presymptomatic testing; prospective; public health relevance; repetitive behavior; research clinical testing; social; success; tool

ABSTRACT The overarching goal of this proposal is to lower the age of detection in autism to early infancy, making presymptomatic (i.e., before the emergence of ASD-specific behavioral features) intervention feasible. Infants with an older autistic sibling have up to a 20% risk of developing autism spectrum disorder (ASD). Prospective high familial risk (HR) infant sibling studies have shown that the defining behaviors of ASD do not emerge until the latter part of the first year and into the second year of life. Therefore, the vast majority of affected children are diagnosed after age 2. No behavioral markers in the first year of life have yet been identified that can predict later ASD diagnosis with sufficient accuracy (i.e., positive predictive value: PPV ≥ 80%) to justify presymptomatic intervention. We recently published two independent approaches that use brain imaging in the first year of life to predict which HR infants will be diagnosed with ASD at 2 years of age. Specifically, structural MRI (sMRI) at 6 and 12 months of age, and resting state functional connectivity MRI (fcMRI) at 6 months of age independently predicted later ASD diagnosis in HR infants with over 80% PPV. Our preliminary data show that a third MRI approach, using regions of CSF volume and cortical shape at 6 months of age can also accurately predict later ASD diagnosis. If we replicate and extend these findings, we will be able to identify individual infants at “ultra-high risk” (80% chance) of developing ASD, rather than being limited to group-level risk (20% chance), where we do not know who will later be affected. This R01 application aims to move our initial findings toward a clinical test for ASD in HR infants in the first year of life. Aim 1 will validate our previous findings in a new, independent sample of HR infants, extend our methods to a new MRI platform, and examine whether fcMRI and/or sMRI, with and without behavioral information, during the presymptomatic period in infancy, accurately predict ASD diagnosis at 24 months of age. Aim 2 will move beyond predicting categorical diagnosis to predicting dimensional, clinically-relevant characteristics for individual infants. Specific dimensional targets include expressive language level, social responsiveness, initiation of joint attention, and repetitive behavior. Validating and extending our findings on presymptomatic prediction of ASD in a new sample, on a different MRI scanner, and with dimensional developmental characteristics are critical next steps for moving the field forward toward (a) the development of a clinically-useful, presymptomatic test for identifying ultra-high risk infants who would benefit from very early intervention in infancy, (b) efficient studies of presymptomatic intervention strategies in individuals at ultra-high risk, and (c) the development of future presymptomatic tests for use in the general (not just HR) population.

摘要 这项提案的首要目标是将自闭症的发现年龄降低到婴儿早期， 症状前（即，在出现ASD特异性行为特征之前）进行干预是可行的。婴儿 如果有一个年长的自闭症兄弟姐妹，患自闭症谱系障碍（ASD）的风险高达20%。前瞻性 高家族风险（HR）婴儿兄弟姐妹的研究表明，ASD的定义行为不会出现，直到 第一年的后半部分和第二年的生活。因此，绝大多数受影响的儿童 2岁以后被诊断出来。在生命的第一年，还没有发现任何行为标记， 以足够的准确度预测以后的ASD诊断（即，阳性预测值：PPV ≥ 80%），以证明 症状前干预我们最近发表了两个独立的方法，使用脑成像在 第一年的生活，以预测哪些HR婴儿将被诊断为ASD在2岁。具体来说，结构 6个月和12个月大时的MRI（sMRI），以及6个月大时的静息状态功能连接MRI（fcMRI）。 年龄独立地预测了PPV超过80%的HR婴儿的ASD诊断。我们的初步数据显示 第三种MRI方法，使用6个月大时的CSF体积和皮质形状区域， 准确预测ASD诊断。如果我们复制并扩展这些发现，我们将能够识别 个别婴儿在“超高风险”（80%的机会）发展ASD，而不是局限于群体水平 风险（20%的机会），我们不知道谁会受到影响。此R 01应用程序旨在将我们的 在出生后第一年对HR婴儿进行ASD临床测试的初步结果。目标1将验证我们之前的 在一个新的，独立的HR婴儿样本中的发现，将我们的方法扩展到一个新的MRI平台，并检查 无论是fcMRI和/或sMRI，有或没有行为信息，在症状前期， 婴儿期，准确预测24个月大时的ASD诊断。目标2将超越预测分类 诊断，以预测个体婴儿的尺寸，临床相关特征。具体 维度目标包括表达性语言水平、社会反应、联合注意的启动， 重复的行为验证和扩展我们在新的ASD症状前预测中的发现， 样本，在不同的MRI扫描仪上，并与尺寸发育特征是关键的下一步 为了推动该领域的发展，（a）开发一种临床有用的症状前测试， 确定将受益于婴儿期非常早期干预的超高风险婴儿，（B）有效研究 在超高风险的个人症状前干预策略，（c）未来的发展 症状前测试用于一般人群（不仅仅是HR）。