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

项目摘要：内化/外化精神病可在3岁前识别， 神经发育风险标记物在婴儿中很明显。尽管对预防的影响很大，但临床上 其影响微乎其微。我们使用创新的计算和流行病学数据科学方法 加速将婴儿期神经发育发现的临床转化为可概括的风险 学龄前儿童精神病理学的预测。我们的主要目标是生成一个实用的临床风险计算器 对于公共卫生用途，幼儿心理健康风险计算器(MHRiskCalc-YC)。要实现 必要的力量和精确度，我们创造了心理健康，早期合成队列(MHESC)，池 华盛顿大学和西北大学的多个校外队列组成了第一个临床- 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，出生后54个月)。使风险计算器的临床和研究效用和成本最大化 有效性，我们将生成一系列为设想的最终用户量身定做的风险算法，结合输入 来自临床利益相关者。算法还将建立出生前和出生后环境因素的附加值 风险预测，这是一个关键但未被充分研究的RDoC要素。目标1优化临床可行性和成本 通过仅从常用调查数据生成MHRiskCalc-YC算法来提高效率 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 建立更密集的评估的统计和临床增量效用，以供将来在精神病学中使用 健康专业设置。该算法顺序地测试方法的附加预测值 中高强度(从直接评估到EEG再到MRI)，以实现最准确、负担最小的风险 预测。Aim 3算法针对神经发育联盟未来的临床研究进行了优化， 将MRI数据的附加值建模到AIM 1算法中。这反映了 并且还将为该联盟生成一份基于经验的最佳实践指南，以 优化神经成像评估的时间/数量。将在婴儿身上建立外部效度 Connectome Project(BCP)。MHESC利用一个前所未有的、对时间敏感的机会 加速广泛预先调整的多种校外活动的科学和临床影响。 以婴儿期为基础的学龄前精神病理学临床风险预测工具对公共健康的影响 在早期识别和预防精神障碍方面改变护理标准的变革性潜力。

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.