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

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，使用基于云的工具来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

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