Utilizing changes in human brain connectivity to establish a dose-response relationship involved in the therapeutic actions of prefrontal brain stimulation on depression symptoms

项目概要/摘要 这是Azeezat K的多样性补充提案。Azeez博士，题为“机器学习预测 难治性抑郁症神经调节治疗的临床结果。这是一个补充， 由医学博士Nolan威廉姆斯持有的父代R 01，标题为“5 R 01 MH 122754 -02：利用人脑变化 连接，以建立涉及前额叶脑治疗作用的剂量-反应关系 抑郁症状的刺激”。父母补助金的目标是（1）测试休息状态的变化 功能连接（rsFC）使用功能磁共振成像（fMRI）扫描每天和（2）检查 rsFC变化如何与新型有效神经调节干预引起的临床改善相关， 斯坦福大学加速智能神经调节疗法（SAINT）。这将有助于我们了解 严重抑郁症（MDD）的潜在机制，特别是难治性抑郁症。 尽管SAINT的高功效，但相对于现有的治疗剂， 参与者没有回应。不作出反应，特别是在TRD中，会导致有害的健康， 对参与者和卫生系统的经济影响。我们无法预测谁会 对治疗的反应构成了SAINT技术中的主要转化差距。因此， 目前的多样性补充是在神经成像数据上使用机器学习算法来预测谁是 最有可能对治疗有反应。数据科学、神经成像和神经刺激正在以一种 令人兴奋的交界处，这些学科的交叉点是多样性补充所在。的组合 机器学习分类器模型（监督和非监督）和选择适当的成像 在训练数据上进行训练，然后进行测试和验证的特征将产生具有高预测精度的模型 临床结果。这些部分的结合将使我们有最大的可能性开发一个成功的 可以打包到软件中以伴随神经调节干预的算法。当前 补充旨在1）对队列之间的治疗反应进行分类;活性、假手术和神经典型对照， 和2）准确预测治疗严重程度分类中的缓解和应答结果。之多样 补充将使Azeez博士能够熟练掌握1）机器学习技术，2）临床 评估，和3）专业发展，而在2年的资助期。训练研究 该项目将在斯坦福大学进行，该大学提供优秀的智力和体力 资源来完成拟议的工作。补充报告中提出的研究将有助于启动博士。 Azeez的职业生涯是为精神医学临床医生开发计算辅助工具。这是一个主要目标， 补充申请和一个将准备候选人，博士。 准备一个有竞争力的NIH K奖，以及作为一个独立的学术生涯的长期目标 研究员这项拟议的工作有可能改善抑郁症患者的生活。

PROJECT SUMMARY/ABSTRACT This is a Diversity Supplement Proposal for Azeezat K. Azeez, Ph.D., entitled “Machine Learning for Predictive Clinical Outcomes to Neuromodulation Therapy for Treatment-Resistant Depression”. It is a Supplement to the Parent R01, held by Nolan Williams, MD titled “5R01MH122754-02: Utilizing changes in human brain connectivity to establish a dose-response relationship involved in the therapeutic actions of prefrontal brain stimulation on depression symptoms”. The goal of the Parent Grant is to (1) test changes in resting-state functional connectivity (rsFC) using functional magnetic resonance imaging (fMRI) scans daily and (2) examine how rsFC changes relate to clinical improvement due to a novel and effective neuromodulation intervention, Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT). This will improve our understanding of the underlying mechanism of Major Depressive Disorder (MDD), particularly Treatment-Resistant Depression. Notwithstanding the high efficacy of SAINT, relative to existing therapeutics a substantial number of participants do fail to respond. Failure to respond, particularly in TRD, leads to detrimental health and economic effects on the participant as well as on the health system. Our lack of ability to predict who will respond to treatment constitutes a major translational gap in the SAINT technology. Therefore, the goal of the current diversity supplement is to employ machine learning algorithms on neuroimaging data to predict who is most likely to respond to treatment. Data science, neuroimaging, and neurostimulation are converging at an exciting junction, the intersection of these disciplines is where the Diversity Supplement lies. A combination of Machine Learning classifier models (supervised and unsupervised) and selection of appropriate imaging features, trained on training data, then tested, and validated will yield a model with high accuracy for predicting clinical outcomes. A combination of these parts will allow us the highest probability of developing a successful algorithm that can be packaged into software to accompany neuromodulation intervention. The current Supplement aims to 1) classify Treatment Response between cohorts; Active, Sham, and Neurotypical Control, and 2) accurately predict remission and response outcomes in Treatment Severity classes. The Diversity Supplement would allow Dr.Azeez to gain proficiency in 1) Machine Learning Techniques, 2) Clinical Assessments, and 3) Professional Development while under the 2-year funding period. Training and research for the project will be conducted at Stanford University which offers excellent intellectual and physical resources to complete the proposed work. The research proposed in the Supplement will help to launch Dr. Azeez’s career in developing Computational Aids for Clinicians in Psychiatric Medicine. This is a major goal of the supplement application and one that will prepare the candidate, Dr. Azeez, for the short-term goal of preparing a competitive NIH K- Award, and the long-term goal for a career as an independent academic researcher. This proposed work has the potential to improve the lives of patients suffering with depression.