Reliable prediction of high-risk infants of autism with cortical microstructural biomarker

摘要 自闭症谱系障碍（ASD）等神经精神障碍的诊断是基于 行为评估，直到2-3岁才能确认。ASD影响59名儿童中的1名， 美国，来自高危家庭的婴儿患ASD的风险是普通家庭的20倍。 人口目前的范例错过了从出生到2-3岁的潜在早期干预的宝贵时间窗口 岁的因此，ASD研究的重点正在转向开发早期生物标志物， 预测婴儿发展未来行为异常的风险，而婴儿仍处于预- 症状期影像学标记物在理解ASD和典型发育中起重要作用 (TD)大脑婴儿早期的神经MRI可能会改善目前诊断高危ASD的模式。 婴儿。常规T1的宏观结构测量，如皮质厚度和表面积 加权MRI（T1 w）已成为表征婴儿皮质成熟的主要测量方法。 然而，T1 w MRI不能显示皮质内复杂的微结构变化的信息 地幔皮质微结构与细胞和分子的基本过程有关， 在神经元回路形成和脑功能出现中的作用。我们最近开发了一部小说 使用高级扩散MRI（dMRI）（包括扩散张量）量化皮质微结构的方案 DTI和扩散峰度成像（DKI）。这些先进的基于dMRI的皮质微结构 测量是表征婴儿的微结构分化的敏感成像标记 皮层成熟我们假设婴儿早期基于dMRI的皮质微结构测量 可能是早期检测高危婴儿ASD的潜在生物标志物。本研究的目的是开发 并测试一种新的协议，使用基于dMRI的皮质微结构特征，以可靠地预测临床评分， 患有ASD和其他脑部疾病的高危婴儿。我们的目标是：1）开发一种技术， 结合了基于dMRI的婴儿早期皮质微结构测量， 机器学习算法可以可靠地预测婴儿2岁时的未来神经发育结果; 2)以证明该技术在ASD高危婴儿中的初步临床实用性。完成这些 我们利用了一个大型的婴儿研究队列，该队列包括100名TD婴儿，他们纵向接受了多项治疗， 在婴儿早期进行模态MRI扫描，并在2岁时进行神经心理学测试，以进行预测 技术，以及一项高风险ASD研究，该研究允许我们在婴儿期对婴儿进行纵向成像， 测试我们开发的技术。最后，应该强调的是，虽然本项目的重点是 关于其在ASD高危婴儿中的临床应用，所开发的方法在以下方面也具有重要的实用性： 检测其他脑障碍（例如精神分裂症、ADHD）的高风险受试者中的行为异常， 在诊断年龄之前。因此，该技术有望产生广泛的临床影响。

Abstract The diagnosis of neuropsychiatric disorders such as autism spectrum disorder (ASD) is based on behavioral assessments that cannot be confirmed until 2-3 years of age. ASD affects 1 in 59 children in the U.S., and infants from high-risk families have 20 times of risk to develop ASD compared to the general population. Current paradigm misses the precious time window for potential early intervention from birth to 2-3 years of age. Therefore, the focus in ASD research is shifting toward developing early biomarkers which can predict the risk of an infant developing future behavioral abnormalities, while the infant is still in pre- symptomatic stage. Imaging markers play major roles in understanding of both ASD and typical developing (TD) brains. Neural MRI at early infancy may improve the current paradigm for diagnosis for ASD in high-risk infants. Macrostructural measurements such as cortical thickness and surface area from conventional T1 weighted MRI (T1w) have been the primary measurements for characterizing the maturation of infant cortex. However, T1w MRI cannot reveal information about the complex microstructural changes inside the cortical mantle. Cortical microstructure, associated with the underlying cellular and molecular processes, plays a vital role in neuronal circuit formation and emergence of brain functions. We have recently developed a novel protocol to quantify the cortical microstructure using advanced diffusion MRI (dMRI), including diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). These advanced dMRI-based cortical microstructural measurements are sensitive imaging markers for the microstructural differentiation that characterizes the infant cortex maturation. We hypothesize that dMRI-based cortical microstructure measurements in early infancy could be potential biomarkers for early detection of ASD in high-risk infants. The goal of this study is to develop and test a novel protocol, using dMRI-based cortical microstructure feature, to reliably predict clinical score of infants at high risk for ASD and other brain disorders in general. We aim: 1) to develop a technique that incorporates dMRI-based cortical microstructural measures at early infancy with cutting-edge multi-kernel machine learning algorithms to reliably predict infants’ future neurodevelopmental outcomes at 2 years of age; 2) to demonstrate the initial clinical utility of the technique in infants at high-risk for ASD. To accomplish these goals, we leverage a large cohort of infant study that consists 100 TD infants who longitudinally undergo multi- modal MRI scans at early infancy and neuropsychological testing at 2 years for developing the prediction techniques, and a high-risk ASD study that allows us to longitudinally image the infants during infancy for testing our developed techniques. Finally, it should be emphasized that, although the present project focuses on its clinical applications in infants at high risk for ASD, the method developed also has important utility in detecting behavioral abnormalities in high risk subjects for other brain disorders (e.g. schizophrenia, ADHD), at a time prior to the age of diagnosis. Thus, this technique is expected to have a broad clinical impact.