Individualized Closed Loop TMS for Working Memory Enhancement

用于增强工作记忆的个性化闭环 TMS

基本信息

批准号：
10204952
负责人：
Yong Fan
金额：
$ 72.36万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10204952
关键词：
Aging Attention deficit hyperactivity disorder Base of the Brain Behavioral Brain Brain imaging Budgets Clinical Code Communication Communities Computer software Data Data Set Dependence Device or Instrument Development Devices Docking Effectiveness Electric Stimulation Ensure Environment Epilepsy Feedback Frequencies Functional Imaging Functional Magnetic Resonance Imaging Image Imaging Techniques Individual Investigation MRI Scans Major Depressive Disorder Memory impairment Mental Health Mental disorders Methods Mood Disorders Movement Neuroanatomy Neurodegenerative Disorders Neurosciences Noise Outcome Participant Pathway Analysis Patients Pattern Pattern Recognition Performance Persons Protocols documentation Reproducibility Research Rest Running Schizophrenia Scientist Seizures Series Short-Term Memory Signal Transduction Site Sleep Source Code Stress Structure Techniques Testing Time Transcranial magnetic stimulation Treatment outcome Variant awake base design health application imaging study implantable device improved multidisciplinary multimodality neuropsychiatry neuroregulation next generation noninvasive brain stimulation novel novel strategies open source portability recurrent neural network repetitive transcranial magnetic stimulation spatiotemporal tool

项目摘要

ABSTRACT The proposed project is designed to increase precision and responsiveness in transcranial magnetic stimulation therapies across the neuropsychiatric spectrum and specifically in working memory deficits which are common across a variety of neuropsychiatric conditions. Cutting edge functional imaging studies suggest that using multiple types of imaging datasets yield more reliable estimates of brain network communication. Our methods yield a combined resting and task fMRI functional network mapping individualized for each participant that will allow precise identification of brain stimulation targets associated with optimal working memory performance (Aim 1). To close the loop in designing TMS protocols that respond to an individual person's brain activation state, we will also develop and test a real-time brain decoder to determine when optimal working memory states are online (Aim 2). By iteratively testing excitatory neuromodulation frequencies at this stimulation site and capturing the relative movement of brain states towards or away from optimal working memory states, we will settle on the optimal frequency for augmenting working memory performance in each individual (Aim 3). We will validate this approach by administering either the `best' or `worst' (random assignment to each participant) neuromodulation protocol across several days then testing working memory performance and brain activation in a final MRI scan session. The multi-modal based TMS targeting and individualized frequency optimization techniques will be based on our findings and packaged into a combined software suite in Docker containers made available to the scientific and clinical community at the conclusion of this project (Aim 4).

抽象的拟议的项目旨在提高经颅磁的精度和响应能力跨神经精神谱，特别是在工作记忆缺陷中的刺激疗法在各种神经精神疾病中很常见。尖端功能成像研究表明使用多种类型的成像数据集产生了更可靠的大脑网络通信估计值。我们的方法产生一个组合的休息和任务fMRI功能网络映射每个人的个性化将允许精确识别与最佳工作相关的大脑刺激目标的参与者内存性能（AIM 1）。在设计响应个人的TMS协议时关闭循环人的大脑激活状态，我们还将开发和测试实时的大脑解码器，以确定何时最佳的工作记忆状态在线（AIM 2）。通过迭代测试兴奋性神经调节该刺激位点的频率并捕获大脑态向或远离的相对运动最佳的工作记忆状态，我们将安装出最佳频率以增加工作记忆每个人的表现（目标3）。我们将通过管理“最佳”或在几天内进行“最坏”（对每个参与者的随机分配）神经调节协议，然后进行测试在最终的MRI扫描课程中工作记忆性能和大脑激活。基于多模式的TMS 定位和个性化的频率优化技术将基于我们的发现，并将其包装到 Docker容器中的一个组合软件套件可用于科学和临床社区的该项目的结论（AIM 4）。