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: 10192840
• 负责人: Matthew D Albaugh
• 金额: $ 18.44万
• 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 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10192840
• 关键词: 12 year old; Address; Adolescence; Adolescent; Affect; Algorithms; Amygdaloid structure; Anxiety; Anxiety Disorders; Area; Award; Base of the Brain; Behavioral; Big Data; Big Data Methods; Birth; Brain; Brain imaging; Categories; Certification; Child; Child Rearing; Childhood; Clinic; Clinical; Communities; Complement; Complex; Computer software; Data; Data Collection; Data Set; Development; Development Plans; Diffusion; Dimensions; Doctor of Philosophy; Early identification; Emotions; Etiology; Family; Family Process; Future; Goals; Health Sciences; Human; Image; Individual; Institution; Intervention; Knowledge; Learning; 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; Problem Sets; Process; Psychiatry; Psychologist; Psychopathology; Questionnaires; ROC Curve; Regression Analysis; Reporting; Research; Research Personnel; Research Project Grants; Rest; Risk; Risk Factors; Running; Sampling; Series; Statistical Data Interpretation; Stress; Structure; Symptoms; System; Testing; Training; Training Activity; Training Programs; Universities; Validation; Vermont; Work; Youth; analytical method; base; behavior measurement; 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; multimodality; neural circuit; neurobiological mechanism; neuroimaging; novel; novel strategies; prediction algorithm; predictive modeling; professor; psychosocial; recruit; research study; 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持牌临床心理学家在精神病学系在 佛蒙特大学。我的长期职业目标是成为一名独立的调查员， 发展神经影像学的策略，研究从出生到成熟的情绪和焦虑心理学。 虽然我接受过纵向结构MRI分析的培训，但我需要进一步培训， 处理和分析最先进的多波段神经成像数据， 大脑连通性的测量。我也缺乏更复杂的分析方法方面的专业知识 更充分地利用大样本多模态数据集。这些方法将使我能够超越 传统的单变量统计分析，并为未来的大数据计划做好准备。在拟议 K 08期间，我的首要目标是发展机器学习方法应用方面的专业知识， 多模态数据，以表征最突出的心理社会和基于大脑的预测青年 内化精神病理学为了实现这些目标，我正在追求职业发展和培训 在以下领域开展活动：1）评估和描述心理社会风险因素; 2）理论和 大数据方法的实施，包括机器学习算法和交叉验证策略; 3） 使用人类连接组项目管道分析多波段多模态脑成像数据， 更全面地评估皮质边缘连接方面; 4）独立运行我自己的 神经影像学研究; 5）开发并提交竞争性R 01申请。为了 为了获得这一专门知识，我建议在几个机构开展培训活动，其中包括维也纳大学。 佛蒙特州、哈佛医学院、麦吉尔大学和俄勒冈州健康与科学大学。研究 K 08提案中的项目旨在为以下跨诊断维度生成风险算法： 精神病理学，使用新的机器学习方法来利用两个最大的纵向 神经影像学样本（IMAGEN和青少年大脑认知发展研究）。这些 风险算法随后将使用一个新的诊所推荐的年轻人样本进行改进，我将 从佛蒙特州的一家精神病门诊招募的作为项目的一部分，我还将测试 这些算法预测治疗反应。这些数据将用作我计划的R 01的试验数据 应用程序.鉴于我提出的方法，这个项目将能够检测复杂的非线性 涉及来自多个领域的风险因素的相互作用。因此，这项工作将提供信息，并有助于 描述了最终导致内化问题的各种病因学途径。最重要的是这 该项目可以在关键时期为早期识别和有针对性的干预战略提供信息， 发展国际化医学。 !