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

大脑对学前精神病理学的优化预测：出生后情绪失调的行为标志：一种计算的、发展的认知神经科学方法

• 批准号: 10577867
• 负责人: JOAN L. LUBY
• 金额: $ 131.14万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-12 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10577867
• 关键词: Acceleration; Age; Algorithms; Assessment tool; Behavior; Behavioral; Behavioral Sciences; Birth; Brain; Cardiovascular system; Child; Chronic Disease; Clinic; Clinical; Clinical Research; Cognitive; Data; Data Science; Data Sources; Development; Diagnostic; Dimensions; Disease; Early Intervention; Early identification; Electroencephalography; Elements; Environmental Risk Factor; Epidemiology; Extramural Activities; Future; Generations; Height; Infant; Interview; Longevity; Magnetic Resonance Imaging; Measurement; Measures; Mental Health; Mental disorders; Methods; Modeling; Neuronal Plasticity; Nursery Schools; Outcome; Pattern; Performance; Phenotype; Predictive Value; Prevention; Primary Care; Properdin; Protocols documentation; Psychopathology; Public Health; Research; Research Domain Criteria; Research Personnel; Resources; Risk; Risk Assessment; Risk Marker; Sampling Studies; Science; Series; Site; Specific qualifier value; Standardization; Survey Methodology; Surveys; Testing; Time; Translations; Uncertainty; Universities; Validation; Washington; affective neuroscience; clinical diagnostics; clinical predictors; clinical risk; clinical translation; cognitive neuroscience; cohort; connectome; cost effectiveness; deep learning; early childhood; early onset; emotion dysregulation; epidemiologic data; imaging modality; imaging science; improved; infancy; innovation; learning strategy; medical specialties; mental disorder prevention; multidisciplinary; multimodality; nervous system disorder; neural; neural correlate; neurodevelopment; neuroimaging; postnatal; prediction algorithm; predictive tools; primary care setting; protective factors; risk prediction; risk prediction model; sound; standard of care; tool; visual tracking

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.

项目摘要：到3岁时可以识别内化/外部化心理病理学 神经发育风险标记婴儿的证据。尽管对预防有很大影响，但临床 影响很小。我们使用创新的计算和流行病学数据科学方法 婴儿期神经发育发现的加速临床翻译朝向可推广的风险 学龄前心理病理学的预测。我们的主要目标是产生务实的临床风险计算器 为了公共卫生，幼儿的心理健康风险计算器（MHRiskcalc-YC）。实现 必要的力量和精确度，我们创建了心理健康，早期的合成队列（MHESC），合并 华盛顿大学和西北大学的多个壁外队列形成了第一个临床 - 富集基于神经发育的临床风险算法的“合成”神经影像学队列 （n = 1,020，其次是来自Birth-54 Mos。）。最大化风险计算器的临床和研究实用程序和成本 有效性，我们将生成针对设想的最终用户量身定制的一系列风险算法，编码输入 来自临床利益相关者。算法还将建立前骨前环境因素的附加值 风险预测，这是一个至关重要但知识的RDOC元素。 AIM 1优化临床可行性和成本 通过生成仅从常用的调查数据得出的MHRiskCalc-YC算法来实现的有效性 优化在初级保健环境中使用的可行性。 AIM 2通过 建立更深入评估的统计和临床增量效用，以供将来的精神使用 健康专业环境。该算法顺序测试 最精确的，最少的，中高强度（从直接评估到脑电图到MRI） 预言。 AIM 3算法被优化，用于在神经发育联盟中的未来临床研究中使用， 对AIM 1算法的MRI数据的附加值进行建模。这反映了“常见”协议的 神经想象财团，还将为财团生成一份经验衍生的最佳实践指南 优化定时/神经影像学评估的数量。婴儿将在婴儿中确定 Connectome项目（BCP）。 MHESC利用了前所未有的时间敏感的机会 多种壁外活动的加速科学和临床影响已被广泛预一致。 基于婴儿期的临床风险预测工具的公共卫生影响学龄前心理病理学的影响 在早期识别和预防精神障碍中改变护理标准的变革潜力。