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: 10054894
• 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/10054894
• 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.

项目总结