Individualized Closed Loop TMS for Working Memory Enhancement

摘要 该项目旨在提高经颅磁共振成像的精度和响应能力。 刺激疗法跨越神经精神谱系，特别是在工作记忆缺陷， 在各种神经精神疾病中很常见尖端的功能成像研究表明 使用多种类型的成像数据集可以产生更可靠的大脑网络通信估计。 我们的方法产生了一个组合的休息和任务功能磁共振成像功能网络映射个性化的每一个 参与者将允许精确识别与最佳工作相关的脑刺激目标 内存性能（目标1）。为了在设计TMS协议时闭合回路， 一个人的大脑激活状态，我们还将开发和测试一个实时的大脑解码器，以确定何时 最佳工作记忆状态是在线的（目标2）。通过反复测试兴奋性神经调节 该刺激部位的频率并捕捉大脑状态朝向或远离的相对运动 最佳的工作记忆状态，我们将决定最佳的频率，以扩大工作记忆 每个人的表现（目标3）。我们将通过管理“最佳”或 几天内的“最差”（随机分配给每位参与者）神经调节方案，然后进行测试 在最后一次核磁共振扫描中，工作记忆表现和大脑激活。基于多模态的TMS 针对性和个性化的频率优化技术将基于我们的研究结果，并打包成 Docker容器中的组合软件套件，可供科学和临床社区使用， 项目目标（4）。

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).