Generalizing and Standardizing fMRI Tools for Differentiating Mental Illnesses and Predicting Medication-Class Response in Patients

泛化和标准化功能磁共振成像工具，用于区分精神疾病和预测患者的药物类别反应

• 批准号: 10264906
• 负责人: Henry Jeremy Bockholt
• 金额: $ 45.15万
• 依托单位: ADVANCED BIOMEDICAL INFORMATICS GROUP
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10264906
• 关键词: Algorithms; Antidepressive Agents; Back; Biological; Biological Markers; Bipolar Disorder; Brain; Brain imaging; Businesses; Classification; Clinic; Clinical Decision Support Systems; Clinical Trials; Clinical assessments; Collaborations; Combination Medication; Communities; Community Surveys; Complex; Consultations; Data; Data Analyses; Data Collection; Data Set; Demography; Depressed mood; Development; Diagnosis; Diagnostic and Statistical Manual of Mental Disorders; Differential Diagnosis; Disease remission; Early Intervention; Ensure; Evaluation; Face; Feedback; Financial cost; Functional Magnetic Resonance Imaging; Future; Generations; Geographic Locations; Goals; Image; Individual; Intuition; Lead; London; Machine Learning; Magnetic Resonance Imaging; Major Depressive Disorder; Manic; Mental Depression; Mental disorders; Minority; Modernization; Mood Disorders; New Mexico; Obsessive-Compulsive Disorder; Ontario; Patients; Pharmaceutical Preparations; Phase; Positioning Attribute; Post-Traumatic Stress Disorders; Procedures; Process; Protocols documentation; Psychiatric Diagnosis; Psychiatrist; Recording of previous events; Reporting; Research; Rest; Running; Scanning; Site; Societies; Standardization; Suicide; Surveys; Symptoms; Technology Transfer; Therapeutic; Time; Training; Universities; Validation; Work; accurate diagnosis; base; biomedical informatics; clinical care; clinical development; clinical trial readiness; cloud based; commercialization; computerized data processing; cost; data acquisition; data repository; data sharing; data tools; depressive symptoms; disability-adjusted life years; effective therapy; emerging adulthood; experience; high risk; imaging biomarker; machine learning method; member; mortality risk; neuroimaging; novel; precision medicine; quality assurance; response; support tools; tool; treatment planning; treatment response; treatment strategy; web portal

Project Summary/Abstract Mood disorders, like many psychiatric conditions, have their onset in emerging adulthood and often last a lifetime. Mental illnesses have an excessively large loading of Disability Adjusted Life Years, making early intervention crucial for both individuals and society. Differentiating bipolar disorder (BD) from major depressive disorder (MDD) based on the Diagnostic and Statistical Manual (DSM) can be especially challenging if clear mania is absent. In fact, BD patients go an average of 6-10 years without the proper diagnosis, with 70% misdiagnosed with MDD instead. The challenge of identifying BD patients among depressed individuals is complex but critical because diagnosis determines treatment. The use of antidepressant (AD) medications in patients with BD can lead to worsening of illness. We have recently shown that, using resting fMRI, we can predict future medication class response with high accuracy (> 90%). In this project we will build on this work to generalize to a new MRI scanner and clinical assessment protocol. In addition we will develop, in consultation with multiple psychiatrists, a cloud-based tool to analyze and report the results from the brain imaging protocol and machine learning analysis, in a timely, meaningful, and interpretable manner. Results are expected to be an important step forward in the eventual development of clinical useful markers of mental illness. 2

项目总结/摘要 情绪障碍，像许多精神疾病一样，在成年初期发病，通常持续一个月。 辈子精神疾病有一个非常大的负荷残疾调整寿命年，使早期 干预对个人和社会都至关重要。双相情感障碍（BD）与抑郁症的区别 根据诊断和统计手册（DSM），如果明确诊断， 没有躁狂症。事实上，BD患者平均6-10年没有得到正确的诊断，70% 被误诊为抑郁症在抑郁个体中识别BD患者的挑战是 复杂但关键，因为诊断决定治疗。抗抑郁药（AD）的使用 BD患者可导致病情恶化。我们最近发现，使用静息功能磁共振成像，我们可以 预测未来的药物类别反应具有高准确性（> 90%）。在本项目中，我们将在此基础上， 推广到新的MRI扫描仪和临床评估方案。此外，我们还将通过协商， 与多名精神科医生合作，一个基于云的工具，用于分析和报告大脑成像协议的结果 和机器学习分析，以及时，有意义和可解释的方式。预计结果将是一个 这是最终开发临床有用的精神疾病标志物的重要一步。 2