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 这是一份针对Azeezat K.Azeez博士的多样性补充提案，题为《机器学习的预测性 神经调节疗法治疗难治性抑郁症的临床结果“。这是一份补充 父母R01，由医学博士诺兰·威廉姆斯持有，标题为“5R01MH122754-02：利用人脑的变化” 建立与额叶前脑治疗活动有关的剂量-反应关系的连通性 对抑郁症状的刺激“。父GRANT的目标是(1)测试静息状态下的变化 使用功能磁共振成像(FMRI)的功能连接(RsFC)每天扫描和(2)检查 RsFC的变化如何与一种新颖有效的神经调节干预的临床改善有关， 斯坦福加速智能神经调节疗法(SAINT)。这将有助于我们更好地理解 主要抑郁障碍(MDD)的潜在机制，特别是治疗难治性抑郁症。 尽管SAINT的疗效很高，但相对于现有的治疗方法，相当数量的 参与者确实没有做出回应。未能做出反应，特别是在TRD中，会导致有害的健康和 对参与者和卫生系统的经济影响。我们缺乏预测谁会的能力 对治疗的反应构成了SAINT技术中的一个重大翻译差距。因此，我们的目标是 目前的多样性补充是对神经成像数据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以 令人兴奋的结合点，这些学科的交叉点就是多样性补充的所在。一种组合 机器学习分类器模型(监督和非监督)和适当成像的选择 要素、根据训练数据进行训练、然后进行测试和验证将生成具有高预测精度的模型 临床结果。这些部分的组合将使我们能够开发出成功的 可以打包到软件中的算法，以伴随神经调节干预。海流 补充剂旨在1)对队列之间的治疗反应进行分类；积极的、假的和神经典型的对照， 2)准确预测治疗严重程度的缓解和应答结果。多样性 补充剂将使Azeez博士精通1)机器学习技术，2)临床 评估，以及3)在两年期资助期内的专业发展。培训和研究 因为该项目将在斯坦福大学进行，该大学提供优秀的智力和体能 完成拟议工作所需的资源。附录中提出的研究将有助于启动Dr。 Azeez在为精神病医学临床医生开发计算辅助工具方面的职业生涯。这是一个主要的目标 补充剂申请和一个将为候选人阿齐兹博士为短期目标做准备的申请 准备一个有竞争力的NIH K奖，以及作为一名独立学者的长期目标 研究员。这项拟议的工作有可能改善抑郁症患者的生活。