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: 10157747
• 负责人: Henry Jeremy Bockholt
• 金额: $ 43.76万
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10157747
• 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; Readiness; 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; cloud based; commercialization; computerized data processing; cost; data acquisition; data sharing; data tools; data warehouse; 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

项目摘要/摘要 情绪障碍，就像许多精神疾病一样，在成年初期发病，通常持续一段时间 一辈子。精神疾病对伤残调整寿命的负担过大，使得 干预对个人和社会都至关重要。双相情感障碍(BD)与抑郁症的鉴别 基于诊断和统计手册(DSM)的障碍(MDD)如果明确，可能会特别具有挑战性 狂热并不存在。事实上，BD患者平均有6-10年没有得到正确的诊断，其中70% 而是误诊为MDD。在抑郁症患者中识别BD患者的挑战是 很复杂，但很关键，因为诊断决定治疗。抗抑郁(AD)药物在中国的使用 BD患者可能会导致病情恶化。我们最近已经证明，使用静息功能磁共振成像，我们可以 预测未来药物类反应的准确性很高(&gt；90%)。在这个项目中，我们将在此工作的基础上 概括为一种新的MRI扫描仪和临床评估方案。此外，我们还将在协商中制定 与多名精神病学家合作，这是一个基于云的工具，用于分析和报告大脑成像协议的结果 和机器学习分析，以一种及时、有意义和可解释的方式。预计结果将是 在最终开发临床上有用的精神疾病标志物方面向前迈出了重要的一步。 2.