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: 10361482
• 负责人: JOAN L. LUBY
• 金额: $ 156.01万
• 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/10361482
• 关键词: Age; Algorithms; Assessment tool; Behavioral; Behavioral Sciences; Birth; Brain; Cardiovascular system; Child; Chronic Disease; Clinic; Clinical; Clinical Research; Cognitive; Data; Data Science; Data Sources; Development; 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 Health Care; Properdin; Protocols documentation; Psychopathology; Public Health; Research; Research Domain Criteria; Research Personnel; Resources; Risk; Risk Assessment; Risk Marker; Sampling Studies; Science; Series; Site; Standardization; Survey Methodology; Surveys; Testing; Time; To specify; Translations; Uncertainty; Universities; Validation; Washington; affective neuroscience; base; brain behavior; 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 correlate; neurodevelopment; neuroimaging; postnatal; prediction algorithm; primary care setting; protective factors; relating to nervous system; risk prediction; 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：内化/外化精神病理学在 3 岁时就可以识别， 婴儿中明显的神经发育风险标志物。尽管对预防具有重大意义，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 加速婴儿期神经发育发现向普遍风险的临床转化 学前精神病理学的预测。我们的主要目标是生成实用的临床风险计算器 用于公共卫生用途的幼儿心理健康风险计算器 (MHRiskCalc-YC)。达到 必要的力量和精度，我们创建了心理健康早期综合队列（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- 丰富的“合成”神经影像队列，用于生成基于神经发育的临床风险算法 （N=1,020，从出生到 54 个月进行跟踪）。最大限度地提高风险计算器的临床和研究效用和成本 为了提高有效性，我们将生成一系列针对预期最终用户量身定制的风险算法，并结合输入 来自临床利益相关者。算法还将确定产前产后环境因素的附加值 风险预测，一个至关重要但尚未得到充分研究的 RDoC 要素。目标 1 优化临床可行性和成本 通过生成仅源自常用调查数据的 MHRiskCalc-YC 算法来提高有效性 优化未来在初级保健机构中使用的可行性。目标 2 通过以下方式优化预测精度 建立更深入的评估的统计和临床增量效用，以供将来在心理治疗中使用 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（从直接评估到脑电图到核磁共振）可实现最精确、负担最小的风险 预言。 Aim 3 算法针对神经发育联盟未来的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“通用”协议 神经影像联盟，还将为联盟生成一个基于经验的最佳实践指南 优化神经影像评估的时间/数量。外部有效性将在婴儿中建立 连接组项目（BCP）。 MHESC 利用前所未有的、时间敏感的机会 加速已广泛预先调整的多项校外活动的科学和临床影响。 基于婴儿期的学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。