Development and clinical validation of multimodal risk algorithms for predicting future internalizing psychopathology

用于预测未来内化精神病理学的多模式风险算法的开发和临床验证

• 批准号: 10646253
• 负责人: Matthew D Albaugh
• 金额: $ 18.42万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646253
• 关键词: 12 year old; Address; Adolescence; Adolescent; Affect; Algorithms; Amygdaloid structure; Anxiety; Anxiety Disorders; Area; Award; Behavioral; Big Data; Big Data Methods; Birth; Brain; Brain imaging; Child; Child Rearing; Childhood; Clinic; Clinical; Communities; Complement; Complex; Computer software; Data; Data Collection; Data Set; Development; Development Plans; Diagnostic; Diffusion; Dimensions; Doctor of Philosophy; Early identification; Elasticity; Emotions; Etiology; Family; Family Process; Future; Goals; Health; Human; Image; Individual; Institution; Intervention; Knowledge; Learning; Licensing; Link; Machine Learning; Magnetic Resonance Imaging; Measures; Medial; Mediating; Mediation; Mental Health; Mentors; Methods; Modeling; Mood Disorders; Moods; Neurobiology; Oregon; Outcome; Outpatients; Parents; Pathway interactions; Prediction of Response to Therapy; Prevention; Probability; Process; Psychiatry; Psychologist; Psychopathology; Qualifying; Questionnaires; ROC Curve; Regression Analysis; Reporting; Research; Research Personnel; Research Project Grants; Rest; Risk; Risk Factors; Running; Sampling; Science; Series; Statistical Data Interpretation; Stress; Symptoms; System; Testing; Training; Training Activity; Training Programs; Universities; Validation; Vermont; Work; Youth; analytical method; behavior measurement; brain based; career; career development; clinically significant; cognitive development; connectome; critical period; data analysis pipeline; early life stress; longitudinal analysis; machine learning algorithm; medical schools; multimodal data; multimodal neuroimaging; multimodality; neural circuit; neurobiological mechanism; neuroimaging; novel; novel strategies; prediction algorithm; predictive modeling; professor; psychosocial; recruit; research study; risk prediction model; symptomatology; theories; tool; tractography; 12 year old; Address; Adolescence; Adolescent; Affect; Algorithms; Amygdaloid structure; Anxiety; Anxiety Disorders; Area; Award; Behavioral; Big Data; Big Data Methods; Birth; Brain; Brain imaging; Child; Child Rearing; Childhood; Clinic; Clinical; Communities; Complement; Complex; Computer software; Data; Data Collection; Data Set; Development; Development Plans; Diagnostic; Diffusion; Dimensions; Doctor of Philosophy; Early identification; Elasticity; Emotions; Etiology; Family; Family Process; Future; Goals; Health; Human; Image; Individual; Institution; Intervention; Knowledge; Learning; Licensing; Link; Machine Learning; Magnetic Resonance Imaging; Measures; Medial; Mediating; Mediation; Mental Health; Mentors; Methods; Modeling; Mood Disorders; Moods; Neurobiology; Oregon; Outcome; Outpatients; Parents; Pathway interactions; Prediction of Response to Therapy; Prevention; Probability; Process; Psychiatry; Psychologist; Psychopathology; Qualifying; Questionnaires; ROC Curve; Regression Analysis; Reporting; Research; Research Personnel; Research Project Grants; Rest; Risk; Risk Factors; Running; Sampling; Science; Series; Statistical Data Interpretation; Stress; Symptoms; System; Testing; Training; Training Activity; Training Programs; Universities; Validation; Vermont; Work; Youth; analytical method; behavior measurement; brain based; career; career development; clinically significant; cognitive development; connectome; critical period; data analysis pipeline; early life stress; longitudinal analysis; machine learning algorithm; medical schools; multimodal data; multimodal neuroimaging; multimodality; neural circuit; neurobiological mechanism; neuroimaging; novel; novel strategies; prediction algorithm; predictive modeling; professor; psychosocial; recruit; research study; risk prediction model; symptomatology; theories; tool; tractography

I am currently Assistant Professor and a licensed clinical psychologist in the Department of Psychiatry at the University of Vermont. My long-term career goal is to become an independent investigator using novel strategies in developmental neuroimaging to study mood and anxiety symptomatology from birth to maturity. Although I have been trained in the analysis of longitudinal structural MRI, I require further training in the processing and analysis of state-of-the-art multiband neuroimaging data that allows for more sensitive measures of brain connectivity. I am also lacking expertise with regard to more sophisticated analytic methods for more fully leveraging large-sample multimodal datasets. Such approaches will enable me to move beyond conventional univariate statistical analyses and prepare me for future Big Data initiatives. During the proposed K08 period, my overarching goal is to develop expertise in the application of machine-learning approaches to multimodal data in order to characterize the most salient psychosocial and brain-based predictors of youth internalizing psychopathology. To achieve these goals, I am pursuing career development and training activities in the following areas: 1) assessment and characterization of psychosocial risk factors; 2) theory and implementation of Big Data methods, including machine learning algorithms and cross-validation strategies; 3) analysis of multiband multimodal brain imaging data using Human Connectome Project pipelines with the aim of more comprehensively assessing aspects of cortico-limbic connectivity; 4) independently running my own neuroimaging research study; and 5) developing and submitting a competitive R01 application. In order to obtain this expertise, I am proposing training activities at several institutions, including the University of Vermont, Harvard Medical School, McGill University, and Oregon Health and Science University. The research project in this K08 proposal aims to produce risk algorithms for a transdiagnostic dimension of psychopathology, using novel machine learning approaches to leverage two of the largest longitudinal neuroimaging samples in the world (IMAGEN and the Adolescent Brain Cognitive Development study). These risk algorithms will subsequently undergo refinement using a new sample of clinic-referred youths that I will recruit from an outpatient psychiatric clinic in Vermont. As part of the project, I will also test the degree to which these algorithms predict treatment response. These data will be used as pilot data for my planned R01 application. Given the methods that I am proposing, this project will be able to detect complex non-linear interactions involving risk factors from a multitude of domains. As a result, this work will inform, and help to delineate, various etiological pathways that ultimately result in internalizing problems. Most importantly, this project could inform early identification and targeted intervention strategies during a critical period for the development of internalizing symptomatology. !

我目前是助理教授和一名持牌临床心理学家 佛蒙特大学。我的长期职业目标是使用小说成为独立研究者 发育神经影像学的策略从出生到成熟度研究情绪和焦虑症状。 尽管我已经接受了纵向结构MRI分析的培训，但我需要进一步的培训 最先进的多播神经影像数据的处理和分析，该数据允许更敏感 大脑连通性的度量。关于更复杂的分析方法，我也缺乏专业知识 为了更充分利用大型多模式数据集。这种方法将使我能够超越 常规的单变量统计分析，并为我准备将来的大数据计划。在提议期间 K08时期，我的总体目标是在应用机器学习方法方面发展专业知识 多模式数据是为了表征青年的最显着的社会心理和基于大脑的预测指标 内部化心理病理学。为了实现这些目标，我正在追求职业发展和培训 以下领域的活动：1）心理社会危险因素的评估和表征； 2）理论和 实施大数据方法，包括机器学习算法和交叉验证策略； 3） 使用人类Connectome项目管道分析多播多模式大脑成像数据的目标 更全面地评估了皮质膜连通性的各个方面； 4）独立运行我自己的 神经影像学研究； 5）开发和提交有竞争力的R01申请。为了 获得此专业知识，我建议在包括大学在内的多个机构进行培训活动 佛蒙特州，哈佛医学院，麦吉尔大学和俄勒冈健康与科学大学。研究 此K08提案中的项目旨在生成用于转诊的风险算法 精神病理学，采用新颖的机器学习方法来利用两个最大的纵向 世界上的神经影像样品（成像和青少年脑认知发展研究）。这些 风险算法随后将使用新的诊所参考青年样本进行改进 从佛蒙特州的门诊精神病诊所招募。作为项目的一部分，我还将测试 这些算法可以预测治疗反应。这些数据将用作我计划的R01的试验数据 应用。考虑到我提出的方法，该项目将能够检测复杂的非线性 涉及来自多个域的风险因素的相互作用。结果，这项工作将告知并帮助 描绘的，各种病因途径最终导致内部化问题。最重要的是，这 项目可以在关键时期为早期识别和针对性的干预策略提供信息 开发内在症状学。 呢