MRI Based Presymptomatic Prediction of ASD

基于 MRI 的 ASD 症状前预测

基本信息

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
项目类别：
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

项目摘要

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.

抽象的该提案的总体目标是将自闭症的检测年龄降低至婴儿早期，从而使症状前（即出现 ASD 特定行为特征之前）对婴儿进行干预是可行的。患有自闭症的兄弟姐妹患自闭症谱系障碍 (ASD) 的风险高达 20%。高家族风险 (HR) 婴儿兄弟姐妹研究表明，自闭症谱系障碍 (ASD) 的定义行为直到因此，绝大多数儿童在第一年的后半段和进入第二年受到影响。 2 岁后被诊断出来。尚未发现生命第一年的行为标志物可以以足够的准确度预测以后的 ASD 诊断（即阳性预测值：PPV ≥ 80%）以证明其合理性我们最近发表了两种使用脑成像的独立方法。出生后第一年预测哪些 HR 婴儿将在 2 岁时被诊断出患有 ASD。 6 个月和 12 个月时的 MRI (sMRI)，以及 6 个月时的静息态功能连接 MRI (fcMRI) 我们的初步数据显示，年龄独立预测了 PPV 超过 80% 的 HR 婴儿的后期 ASD 诊断。第三种 MRI 方法，使用 6 个月大时的脑脊液体积和皮质形状区域，也可以如果我们复制并扩展这些发现，我们将能够准确预测以后的 ASD 诊断。个别婴儿有患自闭症谱系障碍的“超高风险”（80%的机会），而不是局限于群体水平风险（20％的机会），我们不知道谁以后会受到影响。此 R01 应用程序旨在移动我们的。 HR 婴儿出生后第一年自闭症谱系障碍 (ASD) 临床测试的初步结果将验证我们之前的目标。在新的、独立的 HR 婴儿样本中的发现，将我们的方法扩展到新的 MRI 平台，并检查无论是 fcMRI 和/或 sMRI，有或没有行为信息，在症状发生前阶段婴儿期，准确预测 24 个月大时的 ASD 诊断将超越预测分类。诊断以预测个体婴儿的尺寸、临床相关特征。维度目标包括语言表达水平、社会反应能力、共同注意力的启动以及以新的方式验证和扩展我们对 ASD 症状前预测的发现。样本，在不同的 MRI 扫描仪上，并具有尺寸发育特征是接下来的关键步骤推动该领域朝着 (a) 开发一种临床有用的症状前测试识别将从婴儿期早期干预中受益的超高风险婴儿，(b) 有效的研究超高风险个体的症状前干预策略，以及 (c) 未来的发展适用于一般人群（不仅仅是 HR）的症状前测试。