Optimizing prediction of preschool psychopathology from brain: behavior markers of emotion dysregulation from birth: A computational, developmental cognitive neuroscience approach

项目总结：内化/外化精神病理学可在3岁时识别， 神经发育风险标志物在婴儿中很明显。尽管对预防具有强大的意义， 影响很小。我们使用创新的计算和流行病学数据科学方法， 加速婴儿期神经发育发现的临床转化， 学龄前精神病理学的预测我们的主要目标是生成一个实用的临床风险计算器 幼儿心理健康风险计算器（MHRiskCalc-YC）。实现 必要的力量和精度，我们创建了心理健康，早期合成队列（MHESC），汇集 华盛顿大学和西北大学的多个校外队列，形成了第一个临床- 用于生成基于神经发育的临床风险算法的丰富的“合成”神经成像队列 （N= 1，020，从出生开始随访-54个月）。最大化风险计算器的临床和研究效用和成本 有效性，我们将产生一系列的风险算法，为设想的最终用户量身定制， 临床利益相关者。算法还将建立产前产后环境因素的附加值， 风险预测，一个关键但未充分研究的RDoC元素。目标1优化临床可行性和成本 通过生成MHRiskCalc-YC算法，仅从常用的调查数据中获得有效性， 优化未来在初级保健环境中使用的可行性。目标2通过以下方式优化预测精度： 建立更密集评估的统计学和临床增量效用，以供将来在精神疾病中使用 健康专业设置。该算法依次测试以下方法的附加预测值： 中-高强度（从直接评估到EEG到MRI），以获得最精确、负担最小的风险 预测. Aim 3算法针对神经发育联合体的未来临床研究用途进行了优化， 对MRI数据对Aim 1算法的附加值进行建模。这反映了“常见”协议， 神经成像联盟，并将产生一个派生的最佳实践指南，为联盟， 优化神经影像学评估的时间/数量。外部有效性将在婴儿中建立 连接组计划（BCP）。MHESC利用一个前所未有的，时间敏感的机会， 加速已经广泛预先调整的多个校外活动的科学和临床影响。 基于婴儿期的学龄前精神病理学临床风险预测工具对公共卫生的影响 改变早期识别和预防精神障碍的护理标准的变革潜力。

PROJECT SUMMARY: Internalizing/externalizing psychopathologies are identifiable by age 3, with neurodevelopmental risk markers evident in infants. Despite powerful implications for prevention, clinical impact has been minimal. We use innovative computational and epidemiologic data science methods to accelerate clinical translation of neurodevelopmental discovery during infancy towards generalizable risk prediction for preschool psychopathology. Our main objective is generating a pragmatic clinical risk calculator for public health use, the Mental Health Risk Calculator for Young Children (MHRiskCalc-YC). To achieve necessary power and precision, we create the Mental Health, Earlier Synthetic Cohort (MHESC), pooling multiple extramural cohorts at Washington University and Northwestern University to form the first clinically- enriched “synthetic” neuroimaging cohort for generation of neurodevelopmentally-based clinical risk algorithms (N=1,020, followed from birth-54 mos.). To maximize the risk calculator's clinical and research utility and cost effectiveness, we will generate a series of risk algorithms tailored to envisioned end-users, incorporating input from clinical stakeholders. Algorithms will also establish added value of pre-postnatal environmental factors in risk prediction, a crucial but understudied RDoC element. Aim 1 optimizes clinical feasibility and cost effectiveness by generating an MHRiskCalc-YC algorithm derived solely from commonly used survey data to optimize feasibility for future use in primary care settings. Aim 2 optimizes precision of prediction by establishing statistical and clinical incremental utility of more intensive assessment for future use in mental health specialty settings. This algorithm sequentially tests the added predictive value of methods of intermediate-high intensity (from direct assessments to EEG to MRI) for most precise, least burdensome risk prediction. The Aim 3 algorithm is optimized for future clinical research use in neurodevelopmental consortia, modeling the added value of MRI data to the Aim 1 algorithm. This mirrors “common” protocols of neuroimaging consortia and will also generate an empirically-derived best practices guide for consortia to optimize timing/ number of neuroimaging assessments. External validity will be established in the Baby Connectome Project (BCP). The MHESC capitalizes on an unprecedented, time-sensitive opportunity to accelerate scientific and clinical impact of multiple extramural activities that have been extensively pre-aligned. The public health impact of an infancy-based clinical risk prediction tool for preschool psychopathology has transformative potential for altering standard of care in early identification and prevention of mental disorders.