Volume-based Analysis of 6-month Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis

用于自闭症生物标志物识别和早期诊断的 6 个月婴儿脑部 MRI 体积分析

• 批准号: 9243470
• 负责人: Li Wang
• 金额: $ 14.21万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-15 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9243470
• 关键词: 4 year old; Adult; Age; Age-Months; Anatomy; Appearance; Atlases; Autistic Disorder; Behavior; Behavioral; Behavioral Symptoms; Biological Markers; Biometry; Brain; Brain imaging; Centers for Disease Control and Prevention (U.S.); Cerebrum; Child; Clinical; Complex; Data; Data Set; Development; Developmental Disabilities; Diagnosis; Diagnostic; Early Diagnosis; Early Intervention; Early identification; Early treatment; Foundations; Goals; Government; Human; Image; Infant; Infant Development; Intervention; Label; Language; Lead; Learning; Life; Liquid substance; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methods; Multimodal Imaging; Neurobiology; Neurodevelopmental Disorder; Pattern; Play; Positioning Attribute; Prevalence; Probability; Reporting; Research; Research Personnel; Risk; Role; Social Interaction; Solid; Source; Specificity; Stereotyped Behavior; Surveys; System; Tissues; Training; Training Activity; United States; autism spectrum disorder; base; biomarker identification; brain tissue; cognitive neuroscience; cranium; design; developmental neurobiology; disorder prevention; experience; forest; high risk; imaging Segmentation; imaging biomarker; improved; infancy; interest; learning strategy; neonatal brain; neuroimaging; novel; phenomenological models; prevent; skills; social communication; tool; young adult

Project Summary/Abstract Title: Volume-based analysis of 6-month infant brain MRI for autism biomarker identification and early diagnosis Autism spectrum disorder (ASD) is a complex developmental disability, characterized by deficits in social interaction, language skills, repetitive stereotyped behaviors, and restricted interests. Based on a new government survey, it shows 1 in 45 children (ages 3 to 17) are diagnosed with ASD, a significant increase from Centers for Disease Control and Prevention's previously estimated prevalence of 1 in 68 from 2011-2013. Volume-based analysis of neuroimaging data is playing an increasingly critical role in adult autism studies, and has revealed widespread structural and functional abnormalities. However, existing volume-based analysis tools developed for adult brains are ill-suited for infant studies, due to great challenges in brain tissue segmentation and ROI labeling, caused by the extremely low tissue contrast. To become an independent investigator on infant neuroimaging research, the candidate proposes in this K01 application to receive training in clinical phenomenology and child developmental cognitive neuroscience of children with ASD, developmental neurobiology and neurodevelopmental disorders, and biostatistics. These training activities will greatly augment the candidate's background in ASD, infant neuroimaging mapping and establish a solid foundation for his long-term goal of being a leading researcher on developing imaging-based early biological markers for autism. In the research plan, the candidate will create a unique suite of infant-specific, volume-based neuroimaging analysis tools that enable accurate characterization of early brain development in autistic infants, as well as improved capabilities in early identification of biomarkers and early diagnosis of at-risk infants. Specifically, a new method for unified skull stripping and tissue segmentation will be developed (Aim 1). Also, a new atlas-guided multi-channel forest learning will be proposed for ROI labeling (Aim 2). With the accurate tissue segmentation and ROI labeling, ROI-based volume measurements will be performed and used to identify early indicators or biomarker of risk for autism (Aim 3). Finally, early diagnosis of infants will be performed (Aim 4). Results from this research will help identify early biomarkers of risk for autism and also design targeted preemptive intervention strategies. All created tools and atlases will be integrated and released freely to the public, such as through NITRC (www.nitrc.org).

项目摘要/摘要 标题：6个月婴儿脑MRI容量分析用于自闭症生物标记物的识别和 早期诊断 自闭症谱系障碍(ASD)是一种复杂的发育性残疾，其特征是社会功能缺陷 互动、语言技能、重复的刻板行为和受限的兴趣。基于一个新的 政府调查显示，每45名儿童(3岁至17岁)中就有1人被诊断为自闭症，这一数字显著上升 来自疾病控制和预防中心之前估计的2011-2013年间每68人中就有1人患病。 基于体积的神经成像数据分析在成人自闭症研究中发挥着越来越重要的作用， 并揭示了广泛的结构和功能异常。然而，现有的基于体积的分析 由于脑组织方面的巨大挑战，为成人大脑开发的工具不适合婴儿研究。 分割和ROI标记，造成极低的组织对比度。 为了成为婴儿神经影像研究的独立研究员，候选人在本K01中提议 申请接受临床现象学和儿童认知神经科学的培训 自闭症儿童，发育神经生物学和神经发育障碍，以及生物统计学。这些 培训活动将极大地增强应聘者在ASD、婴儿神经成像和 为他的长期目标奠定了坚实的基础，即成为基于成像的开发的领先研究人员 自闭症的早期生物标志物。 在研究计划中，候选人将创建一套独特的针对婴儿的、以体积为基础的 神经成像分析工具，使准确描述早期大脑发育在 自闭症婴儿，以及在早期识别生物标记物和早期 高危婴儿的诊断。具体地说，一种统一的颅骨剥离和组织分割的新方法将 被开发(目标1)。此外，还将提出一种新的地图集引导的多通道森林学习用于ROI标记 (目标2)。随着准确的组织分割和ROI标记，基于ROI的体积测量将被 执行并用于确定自闭症风险的早期指标或生物标记物(目标3)。最后，早期诊断 将对婴儿进行培训(目标4)。这项研究的结果将有助于识别早期的风险生物标志物 并设计有针对性的先发制人干预策略。所有创建的工具和地图集都将 整合并免费向公众发布，例如通过NITRC(www.nitrc.org)。