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

利用人脑连接的变化建立剂量反应关系，参与前额叶脑刺激对抑郁症状的治疗作用

• 批准号: 10542288
• 负责人: Nolan R. Williams
• 金额: $ 11.7万
• 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: 2020-04-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10542288
• 关键词: Algorithms; Brain; Clinical; Clinical assessments; Computer software; Data; Data Science; Development; Discipline; Disease remission; Doctor of Philosophy; Dose; Failure; Funding; Goals; Health system; Human; Image; Intelligence; Intervention; K-Series Research Career Programs; Machine Learning; Major Depressive Disorder; Medicine; Mental Depression; Modeling; Outcome; Parents; Participant; Patients; Probability; Research; Research Personnel; Research Project Grants; Resources; Rest; Severities; Supervision; Techniques; Technology; Testing; Therapeutic; Training; United States National Institutes of Health; Universities; Work; career; cohort; depressive symptoms; functional MRI scan; health economics; improved; ineffective therapies; machine learning algorithm; machine learning classifier; neuroimaging; neuroregulation; novel; parent grant; personalized medicine; predict clinical outcome; predicting response; response; treatment planning; treatment response; treatment-resistant depression

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.

项目摘要/摘要 这是Azeezat K. Azeez博士的一项多样性补充建议，名为“用于预测的机器学习 神经调节疗法的临床结局用于治疗抑郁症”。这是对 由诺兰·威廉姆斯（Nolan Williams）持有的父母R01，标题为“ 5R01MH122754-02：利用人脑的变化 连通性以建立参与前额叶大脑治疗作用的剂量反应关系 对抑郁症状的刺激”。父母赠款的目的是（1）静止状态的测试变化 使用功能磁共振成像（fMRI）扫描的功能连通性（RSFC），（2）检查 RSFC的变化与由于新颖有效的神经调节干预措施而导致的临床改善有关， 斯坦福大学加速了智能神经调节疗法（SAINT）。这将提高我们对 重度抑郁症（MDD）的潜在机制，尤其是抗治疗抑郁症。 尽管圣人的效率很高，但相对于现有治疗 参与者确实无法做出回应。没有做出响应，特别是在TRD中，会导致有害的健康和 对参与者以及卫生系统的经济影响。我们缺乏预测谁将 对治疗的反应构成了圣技术的主要翻译差距。因此， 当前的多样性补充是在神经影像数据上使用机器学习算法来预测谁是 最有可能对治疗做出反应。数据科学，神经影像学和神经刺激正在融合 令人兴奋的交界处，这些学科的交汇处是多样性补充的所在。组合 机器学习分类器模型（监督和无监督）和选择适当的成像 经过训练数据培训的功能，然后进行了测试和验证，将产生一个高准确性的模型 临床结果。这些零件的结合将使我们有最高的可能性发展成功的可能性 可以包装到软件中以适应神经调节干预的算法。电流 补充旨在1）对队列之间的治疗反应进行分类；活跃，假和神经型控制， 2）准确预测治疗严重性类别中的缓解和反应结果。多样性 补充剂将使Azeez博士能够熟练1）机器学习技术，2）临床 评估和3）在2年资金期内的专业发展。培训和研究 该项目将在斯坦福大学进行，该大学提供出色的知识和物理 资源以完成拟议的工作。补品中提出的研究将有助于启动Dr. Azeez在开发精神病医学临床医生的计算辅助工具方面的职业。这是一个主要目标 补充申请以及将为候选人Azeez博士准备的申请，以实现短期目标 准备有竞争力的NIH K奖，也是作为独立学术职业的长期目标 研究员。这项拟议的工作有可能改善患有抑郁症患者的生活。