Individualized Closed Loop TMS for Working Memory Enhancement

摘要 拟议的项目旨在提高经颅磁的精确度和响应性。 神经精神病学领域的刺激疗法，特别是在工作记忆缺陷方面 在各种神经精神疾病中都很常见。尖端功能成像研究表明 使用多种类型的成像数据集可以对大脑网络通信产生更可靠的估计。 我们的方法产生了针对每个个体的组合的静息和任务fMRI功能网络映射 参与者，将允许精确识别与最佳工作相关的大脑刺激目标 内存性能(目标1)。在设计响应个人的TMS协议时闭合循环 人的大脑的激活状态，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 这个刺激部位的频率，并捕捉大脑状态朝向或远离的相对运动 最佳工作记忆状态，我们将确定增加工作记忆的最佳频率 每个人的表现(目标3)。我们将通过管理“Best”或 “最差的”(随机分配给每个参与者)几天的神经调节方案，然后测试 在最后一次核磁共振扫描过程中的工作记忆表现和大脑激活。基于多模式的交通管理系统 定向和个性化频率优化技术将基于我们的研究结果，并打包成 Docker Containers中的组合软件套件提供给科学和临床社区，网址为 本项目的结论(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).