MRI STUDIES OF EARLY BRAIN DEVELOPMENT IN AUTISM

早期大脑过度生长是最近发现的，但尚未直接测量自闭症的现象。 对年轻自闭症患者大脑的直接MRI测量来自五项研究，这些研究发现大脑过度生长， 平均研究年龄为2.7至3.9岁。受影响的大脑区域介导高级社会，情感，语言 和认知功能的特征。自闭症早期大脑发育的MRI研究尚不存在。 自闭症患者大脑发育的间接证据来自对头部的回顾性分析。 周长（HC）。在第一项研究中，我们报告说，HC是正常的平均水平， 在那些后来表现出自闭症的人中，HC在出生时比正常人略小，但在大约12个月时， 异常大。从年龄相关的HC变化推断大脑生长速率，并将其与五个 对2到4岁奥尔兹的MRI大脑大小的研究使我们假设自闭症可能涉及短暂的和年龄限制的 在生命的头两年里大脑过度发育的时期。 根据仅有的两项自闭症前瞻性研究，12个月也大约是 第一个发现自闭症行为异常的年龄。神经回路的形成 在这一发展时期处于最旺盛和最脆弱的阶段。异常连接和 这一过程的中断导致的神经功能障碍可能是自闭症发展的关键。 行为。因此，自闭症患者生命的最初几年提供了一个独特的机会来跟踪同时出现的 自闭症解剖学和临床表型的表达，并建立它们之间的关系， 潜在的因果机制，如影响大脑发育的基因和遗传途径。 我们将通过纵向MRI扫描高危婴儿来确定ASD的早期脑生长生物标志物 对于ASD，有发育迟缓风险的婴儿和12个月和24个月的典型婴儿。我们将获得 体积、面积、皮质厚度、各向异性分数和表观扩散率的区域特异性测量 系数和发展变化值。每个区域的详细解剖图将被导出， 每个年龄段的婴儿。在36个月大时，最终的最佳估计诊断评估将确定哪些风险 婴儿是ASD，DD或其他。这些ASD和DD婴儿和典型婴儿的MRI结果将 统计学比较和早期ASD脑生长异常确定。解剖测量将是 确定了表征和预测ASD早期发育临床表型的方法， C与综合生物统计学核心D合作将确定。我们将与项目4和 综合生物统计学核心D，使用大脑生长生物标志物发现过度生长易感性 基因和途径。通过核心D，我们将识别出明显不同的大脑的单独集群， ASD婴儿中的发育不良表型。

Early brain overgrowth is a recently identified, but not yet directly measured, phenomenon in autism. Direct MRI measurement of the young autistic brain comes from five studies that found brain overgrowth at mean study ages of 2.7 to 3.9 yrs. Affected brain regions mediate higher-order social, emotional, language and cognitive functions that characterize autism. MRI studies of early brain growth in autism are absent. Indirect evidence of brain growth in autism comes from retrospective analyses of head circumference (HC). In the first study to examine the question, we reported that HC was normal average to slightly smaller than normal at birth in those who later manifested autism, but by about 12 months HC was abnormally large. Inferring brain growth rates from age-related changes in HC and placing that with the five MRI brain size studies of 2 to 4 year olds led us to the hypothesis that autism may involve a brief and agedelimited period of abnormal brain overgrowth during the first two years of life. According to the only two existent prospective studies of autism, 12 months is also approximately the first age at which autistic behavioral abnormalities first become detectable. The formation of neural circuitry is at its most exuberant and vulnerable stage during this period of development. Aberrant connectivity and neural dysfunction resulting from disruptions to this process may be key to the development of autistic behaviors. Thus, the first years of life in autism offer a unique chance to track the simultaneous emerging expression of the autistic anatomical and clinical phenotype and establish their relationship to each other and to underlying causal mechanisms, such as genes and genetic pathways that affect brain growth. We will identify early brain growth biomarkers in ASD by longitudinally MRI scanning infants at-risk for ASD, infants at-risk for developmental delay and typical infants at 12 and 24 months. We will obtain region-specific measures of volume, area, cortical thickness, fractional anisotropy and apparent diffusivity coefficient and developmental change values. Detailed anatomic maps of each region will be derived for each infant at each age. At age 36 months, a final best estimate diagnostic evaluation will identify which atrisk infants are ASD, DD or other. MRI results from these ASD and DD infants and typical infants will be statistically compared and early ASD brain growth abnormalities identified. Anatomical measures will be identified that characterize and predict the early developmental clinical phenotype of ASD that Cores B and C in collaboration with the Integrated Biostatistics Core D will identify. We will work with Project 4 and the Integrated Biostatistics Core D to use the brain growth biomarkers to discover overgrowth susceptibility genes and pathways. With Core D, we will identify separate clusters of distinctly different brain maldevelopment phenotypes among the ASD infants.