Integration of markers across physiologic, behavioral, and self-report levels at baseline and in response to treatment to characterize novel subtypes in youth with ADHD

在基线和治疗反应中整合生理、行为和自我报告水平的标志物，以表征患有多动症的青少年的新亚型

• 批准号: 10231262
• 负责人: John Leikauf
• 金额: $ 19.35万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10231262
• 关键词: Address; Adolescent; Affect; Age; Area; Attention; Attention deficit hyperactivity disorder; Award; Behavioral; Behavioral Symptoms; Biological; Caregivers; Caring; Child; Childhood; Classification; Clinical; Clinical Data; Clinical Research; Clinical Trials; Cognition; Cognitive; Cognitive deficits; Collaborations; Collection; DSM-V; Data; Data Set; Databases; Development; Diagnosis; Diagnostic; Digit structure; Disease; Disease model; Electroencephalography; Emotional disorder; Family; Foundations; Functional disorder; Funding; Future; Goals; Harm Reduction; Heterogeneity; Impaired cognition; Impairment; Individual; Individual Differences; K-Series Research Career Programs; Knowledge; Knowledge acquisition; Lead; Link; Longitudinal Studies; Machine Learning; Measures; Mental Health; Mental disorders; Mentors; Mentorship; Methods; Modeling; Modernization; Neurobiology; Neurocognition; Neurologic Deficit; Neuropsychology; Outcome; Parents; Participant; Patient Self-Report; Pharmaceutical Preparations; Phenotype; Physiological; Physiology; Prospective Studies; Psychiatrist; Public Health; Reporting; Research; Research Personnel; Ritalin; Sampling; Selection for Treatments; Short-Term Memory; Statistical Methods; Subgroup; Symptoms; Techniques; Testing; Therapeutic Trials; Time; Training; Translational Research; Treatment outcome; United States National Institutes of Health; Validation; Visit; Work; Youth; atomoxetine; base; biological heterogeneity; cognitive testing; cost effective; diagnostic biomarker; disorder subtype; experience; functional disability; functional outcomes; improved; inattention; interest; longitudinal dataset; multidimensional data; novel; patient oriented; personalized care; personalized diagnostics; personalized medicine; personalized strategies; personalized therapeutic; practice setting; predictive marker; prevent; programs; prospective; psychosocial; recruit; response; skills; statistical and machine learning; sustained attention; symptomatic improvement; therapeutic target; tool; translational clinical trial; treatment response

Project Summary This Mentored Patient-Oriented Career Development Award (K23) will facilitate Dr. Leikauf’s development into an independent researcher and child psychiatrist. It will provide the foundation for him to meaningfully improve strategies for cost-effective personalization of care for ADHD and related emotional disorders in diverse practice settings through a deeper understanding of the underlying phenotypic heterogeneity of these disorders. Attention-Deficit/Hyperactivity Disorder (ADHD) is highly prevalent and functionally impairing, yet phenotypically heterogeneous. Despite the complexity of the phenotype, the diagnosis is most often made using DSM-based behavioral symptom rating scales during short visits in busy pediatric settings. Additionally, current treatments are widely used but have not been demonstrated to improve functional outcomes. Currently, treatment selection involves significant trial-and-error based on caregiver’s subjective report of symptomatic improvement. Cost-effective, objective measures that would aid in personalized treatment selection and address the full range of dysfunction for children with ADHD are critically needed. The heterogeneity of the disorder has limited the development of such tools. Until now, the field has relied on subtypes that have limited validity and are based exclusively on symptom rating scales. Recent developments in our ability to collect and analyze multidimensional data for individual subjects provide a new opening for progress in this crucial area. The project will use two multi-dimensional datasets from completed clinical trials as well as generate data from a smaller, prospective study. The anticipated outcomes are as follows: 1) identification of more phenotypically homogeneous groups of children with ADHD at baseline 2) identification of more homogeneous groups based on creating a multivariate classifier informed by mechanistic response to treatment, and 3) validation of classification schema. Dr. Leikauf’s specific training goals, in collaboration with his mentorship team, include acquisition of knowledge, experience, and skills in the following areas relevant to the proposed work: 1) modern machine-learning statistical techniques that allow reliable inferences to be drawn from high dimensional data with non-linear relationships between variables; 2) mentored experience conducting a multi- method/multi-dimensional prospective study with the goal of identifying personalized therapeutic targets 3) additional translational research/clinical experience to understand what is currently known about ADHD and its relationship to cognitive dysfunction including working memory, sustained attention, and response inhibition, and 4) training in acquisition and analysis of EEG. The results should have immediate clinical impact and provide the foundation for a future prospective personalized therapeutics trial. The experiences in this proposal, if awarded, will also develop Dr. Leikauf’s abilities and thereby enable him to have a fundamental impact on mental health treatment for children and adolescents.

项目摘要 这个指导病人为导向的职业发展奖（K23）将促进博士Leikauf的发展， 独立研究员和儿童精神病学家这将为他提供有意义的进步的基础 策略的成本效益的个性化护理多动症和相关的情绪障碍，在不同的 通过更深入地了解这些潜在的表型异质性， 紊乱注意力缺陷/多动障碍（ADHD）是非常普遍和功能受损，但 表型异质性尽管表型很复杂，但诊断通常是 在忙碌的儿科环境中，使用基于DSM的行为症状评定量表进行短期访视。此外，本发明还 目前的治疗方法被广泛使用，但尚未证明能改善功能结果。目前， 治疗选择涉及基于护理者对症状的主观报告的重大试验和错误 改进.具有成本效益的客观措施，有助于个性化治疗选择， 解决ADHD儿童的全方位功能障碍是迫切需要的。的异质性 无序限制了这些工具的发展。到目前为止，该领域一直依赖于亚型， 有效性，并完全基于症状评定量表。最近我们收集和 分析个体受试者的多维数据为这一关键领域的进展提供了一个新的开端。 该项目将使用来自已完成临床试验的两个多维数据集，并从 一项规模较小的前瞻性研究预期结果如下：1）鉴定出更多的表型 基线时ADHD儿童的同质组2）基于更同质组的识别 通过对治疗的机械反应来创建多变量分类器，以及3）验证 分类模式Leikauf博士与他的导师团队合作的具体培训目标包括 获得与拟议工作相关的以下领域的知识、经验和技能：1） 现代机器学习统计技术，允许从高水平得出可靠的推论， 变量之间具有非线性关系的多维数据; 2）指导经验， 方法/多维前瞻性研究，目的是确定个性化治疗靶点3） 额外的转化研究/临床经验，以了解目前已知的ADHD及其 与认知功能障碍的关系，包括工作记忆、持续注意力和反应抑制， （4）脑电采集与分析训练。结果应该具有立即的临床影响， 为未来的前瞻性个性化治疗试验提供基础。在这方面的经验 一项建议，如果获得，也将发展博士Leikauf的能力，从而使他有一个基本的 对儿童和青少年心理健康治疗的影响。