Prediction of outcomes for deep brain stimulation using multimodal MRI

使用多模态 MRI 预测深部脑刺激的结果

• 批准号: 10371436
• 负责人: John Robert Younce
• 金额: $ 18.94万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10371436
• 关键词: Affect; Anterior; Area; Award; Behavioral; Brain Diseases; Caring; Cerebellum; Clinical; Clinical Data; Clinical Research; Cognitive; Data; Data Sources; Deep Brain Stimulation; Degenerative Disorder; Dementia; Diffusion; Disease; Dopa; Environment; Ethics; Evaluation; Functional Magnetic Resonance Imaging; Funding; Future; Globus Pallidus; Goals; Image; Individual; Intervention Studies; K-Series Research Career Programs; Lateral; Lead; Levodopa; Link; Magnetic Resonance Imaging; Measures; Medical; Mentors; Mentorship; Methods; Modeling; Moods; Motor; Motor Cortex; Multimodal Imaging; Neurobehavioral Manifestations; Neuropsychology; Operative Surgical Procedures; Outcome; Parkinson Disease; Participant; Patient Selection; Patients; Persons; Predictive Factor; Prefrontal Cortex; Prospective cohort; Research; Research Design; Research Personnel; Rest; Retrospective cohort; STN stimulation; Scientist; Short-Term Memory; Source; Statistical Data Interpretation; Statistical Models; Structure of subthalamic nucleus; Techniques; Testing; Thalamic structure; Thick; Training; Training Support; Validation; Ventricular; adverse outcome; base; clinically relevant; cohort; common symptom; demographics; disability; disabling symptom; experience; feature selection; improved; improved outcome; model development; motor symptom; multimodality; nervous system disorder; neuroimaging; neuroregulation; novel; outcome prediction; patient oriented; predictive modeling; predictive tools; programs; prospective; recruit; response; side effect; skills; supervised learning; tool; treatment optimization

PROJECT SUMMARY/ABSTRACT Parkinson disease (PD) is a common and growing source of disability worldwide, and adjunctive surgical treatments are often necessary to optimize treatment in more advanced stages of the disease once medical therapy becomes insufficient. Deep brain stimulation (DBS) has become the most common and effective surgical neuromodulatory technique for motor symptoms of PD, however selection of patients and surgical targets is limited due to an incomplete understanding of predictive factors for DBS outcomes. Historically, levodopa responsiveness has been the primary factor used to establish DBS candidacy, but this correlates only modestly with motor benefit, does not predict cognitive and psychiatric effects, and offers little information on optimal DBS target. Alternative preoperative predictors based on demographics, volumetric analysis, and functional connectivity have been described, but these predictors vary across studies and have not been validated for clinical use. As such, a comprehensive understanding of clinical and neuroimaging predictors of DBS effects is necessary to improve outcomes for those with advanced PD. The purpose of this Mentored Patient-Oriented Career Development Award (K23) is to enable the candidate to develop a funded research program focused on the creation of a model of DBS effects, integrating clinical data and preoperative MRI data to improve patient and target selection in DBS for PD. The candidate's long term goal is to become an independent clinician-scientist investigator capable of developing and implementing clinical neuroimaging tools to investigate DBS mechanisms, improve DBS outcomes, and develop novel DBS applications for individuals with PD and other neurological disorders. Training and mentorship are proposed in three key areas: (1) conducting clinical research, including study design and ethical conduct, (2) performing advanced statistical analyses including multivariate modeling , and (3) obtaining additional skills in neuroimaging tools and analysis, including functional and structural MRI. The research plan for this award will be supported by the training plan as well as an expert team of mentors and an outstanding institutional research environment. The objective of this plan is to develop and validate a model of DBS responsiveness, integrating clinical data and preoperative MRI data, which will improve patient and target selection in DBS for PD. Specifically, this project aims to (1) build a model of motor response to DBS of the subthalamic nucleus (STN) using preoperative behavioral, volumetric, structural and functional connectivity measures, (2) validate this model using prospective data of individuals undergoing STN DBS for PD, and (3) construct similar predictive models for cognitive and psychiatric outcomes of STN DBS. This will set the stage for future interventional studies using this model as a basis for patient and target selection, and lead to use of more comprehensive objective behavioral and neuroimaging data in the preoperative evaluation and planning for DBS in PD.

项目总结/摘要 帕金森病（PD）是一种常见的和日益增长的残疾来源，在世界范围内， 一旦医学上的治疗，在疾病的更晚期阶段， 治疗变得不足。脑深部电刺激（DBS）已成为最常见和最有效的外科手术， 神经调节技术用于PD的运动症状，然而患者和手术靶点的选择是 由于对DBS结果的预测因素了解不完全，因此受到限制。历史上，左旋多巴 反应性一直是确定DBS候选人资格的主要因素，但这只是适度相关 与运动受益，不能预测认知和精神影响，并提供最佳DBS的信息很少 目标基于人口统计学、体积分析和功能的替代术前预测因素 已经描述了连接性，但这些预测因素在不同的研究中有所不同，并且尚未得到验证 临床应用。因此，全面了解DBS效应的临床和神经影像学预测因素， 改善晚期PD患者的预后。 这个指导病人为导向的职业发展奖（K23）的目的是使 候选人开发一个资助的研究计划，重点是创造一个模型的DBS效应，整合 临床数据和术前MRI数据，以改善DBS治疗PD的患者和靶点选择。候选人的 长期目标是成为一名独立的临床科学家研究者，能够开发和 实施临床神经成像工具，以研究DBS机制，改善DBS结局，并开发 新型DBS应用于PD和其他神经系统疾病患者。培训和指导是 建议在三个关键领域：（1）进行临床研究，包括研究设计和道德行为，（2） 执行高级统计分析，包括多变量建模，以及（3）获得额外的技能， 神经成像工具和分析，包括功能和结构MRI。该奖项的研究计划将 得到培训计划以及导师专家团队和杰出机构研究的支持 环境该计划的目标是开发和验证DBS响应模型， 临床数据和术前MRI数据，这将改善DBS治疗PD的患者和靶点选择。 具体而言，本项目旨在（1）建立丘脑底核（subthalamic nucleus，DBS）运动反应模型 使用术前行为、体积、结构和功能连接测量，（2）验证该模型 使用接受PDDBS治疗PD的个体的前瞻性数据，以及（3）构建类似的预测模型 认知和精神方面的结果。这将为未来的干预性研究奠定基础， 该模型作为患者和目标选择的依据，并导致使用更全面的客观行为 和神经影像学数据在PD DBS术前评估和计划中的应用。