Adaptive Neuromodulation of Working Memory Networks in Aging and Dementia

衰老和痴呆症中工作记忆网络的自适应神经调节

• 批准号: 10701758
• 负责人: Simon W Davis
• 金额: $ 75.44万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10701758
• 关键词: Accounting; Address; Adult; Aging; Alzheimer' s Disease; Alzheimer' s disease risk; Award; Behavior; Behavioral; Biological Assay; Biological Markers; Blood Vessels; Brain; Brain Injuries; Brain region; Cerebrovascular Disorders; Clinical; Clinical Trials; Cognition; Cognition Disorders; Cognitive; Dementia; Disease; Disease Progression; Dose; Elderly; Electroencephalography; Failure; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Human; Impairment; Individual; Intervention; Link; Maps; Measures; Mediating; Mediation; Memory; Memory impairment; Modeling; Monitor; Nature; Network-based; Neuronal Plasticity; Neurosciences; Outcome; Patients; Pattern; Performance; Persons; Pharmacotherapy; Population; Predictive Factor; Prefrontal Cortex; Process; Protocols documentation; Public Health; Reaction; Resolution; Risk; Scheme; Short-Term Memory; Site; Syndrome; System; Techniques; Time; Titrations; Transcranial magnetic stimulation; Work; brain health; care costs; cerebrovascular pathology; clinical application; cognitive ability; cognitive function; cognitive performance; cohort; combat; comparison control; computer framework; density; disorder risk; dosage; experimental study; flexibility; functional magnetic resonance imaging/electroencephalography; healthy aging; improved; individual response; insight; memory encoding; mild cognitive impairment; mind control; multimodality; neglect; network models; neural; neural circuit; neurodegenerative dementia; neurophysiology; neuroregulation; neurovascular; noninvasive brain stimulation; novel; preclinical study; recruit; response; spatiotemporal; success

PROJECT SUMMARY Dementia due to Alzheimer’s disease (AD) is a leading public health concern in the US with enormous care costs and no effective pharmacotherapy despite multiple clinical trials. Multiple studies have shown mild cognitive impairment (MCI) to be a precursor risk for AD and to be more amenable to intervention. While preclinical studies have shown that directly modulating activity in the prefrontal cortex (PFC) using non-invasive brain stimulation techniques, such as transcranial magnetic stimulation (TMS), can modulate cognitive function in healthy older adults, there is little evidence of reliable efficacy in MCI. We posit three reasons for this lack of efficacy. First, there is no established means of estimating a reliable biomarker as well as a unique dose-response relationship between TMS intensity and brain activity, which remains a fundamental means of titrating individualized response to neuromodulation. Second, standard TMS protocols fail to capture the dynamic nature of cognitive states and the reaction of endogenous brain states to exogenous neuromodulation. By understanding the dynamic changes associated with a successful brain state, it should be possible to manipulate PFC dynamically in a manner that enhances cognition. Third, no studies using TMS in AD-related populations have accounted for the influence of cerebrovascular disease in the response to TMS. We propose to address these shortcomings by using closed-loop TMS, based on individualized brain networks to establish parameters that can reliably control brain states during normal memory functioning in healthy aging and MCI. To achieve this goal, we will study network activation and neural oscillatory mechanisms underlying the network that regulates working memory (WM), a cognition function with a reliable PFC-based network characterization. We will then target this network using closed-loop TMS to the PFC and measure the impact on WM performance and task-based neural activity. This approach, which builds on our existing K01, U01, and RF1 awards, uses concurrent TMS-fMRI to identify dose-response relationships in the working memory network, which can be used to identify neuroplasticity and optimize targeting for TMS (Aim 1). Next, we apply novel closed-loop TMS to perturb this network using temporally-precise TMS-EEG (Aim 2), optimizing the encoding of memory by minimizing endogenous alpha oscillations. Lastly, we will integrate information collected via fMRI and EEG into a single computational framework in order to model spatiotemporal dynamics of the global brain network, accounting for the influence of both connectivity and cerebrovascular pathology in predicting the success of the TMS-related response in our MCI cohort (Aim 3). In sum, the project will use cutting-edge brain stimulation and network modeling techniques to enhance WM in healthy older adults and MCI and will provide a demonstration of the value of closed-loop, network-guided TMS for future clinical applications.

项目概要 阿尔茨海默病 (AD) 引起的痴呆症是美国主要的公共卫生问题，造成巨大的护理费用 尽管进行了多项临床试验，但仍没有有效的药物治疗。多项研究表明轻度认知 损伤（MCI）是 AD 的先兆风险，并且更容易干预。虽然临床前研究 研究表明，使用非侵入性脑刺激直接调节前额皮质 (PFC) 的活动 经颅磁刺激 (TMS) 等技术可以调节健康老年人的认知功能 对于成人，几乎没有证据表明 MCI 具有可靠的疗效。我们提出了这种缺乏功效的三个原因。第一的， 没有既定的方法来估计可靠的生物标志物以及独特的剂量反应关系 TMS 强度和大脑活动之间的关系，这仍然是滴定个性化的基本手段 对神经调节的反应。其次，标准 TMS 协议无法捕捉认知的动态本质 状态以及内源性大脑状态对外源性神经调节的反应。通过了解 与成功的大脑状态相关的动态变化，应该可以动态地操纵 PFC 以增强认知的方式。第三，没有研究在 AD 相关人群中使用 TMS 来解释 脑血管疾病对 TMS 反应的影响。我们建议解决这些缺点 通过使用闭环TMS，基于个体化的大脑网络建立能够可靠地 在健康衰老和 MCI 中控制正常记忆功能期间的大脑状态。 为了实现这一目标，我们将研究网络激活和网络底层的神经振荡机制 调节工作记忆 (WM)，这是一种具有可靠的基于 PFC 的网络特征的认知功能。 然后，我们将使用闭环 TMS 将该网络定位到 PFC，并测量对 WM 性能的影响 和基于任务的神经活动。这种方法建立在我们现有的 K01、U01 和 RF1 奖项的基础上，使用 并发 TMS-fMRI 来识别工作记忆网络中的剂量反应关系，可用于 识别神经可塑性并优化 TMS 的靶向（目标 1）。接下来，我们将新颖的闭环 TMS 应用于 使用时间精确的 TMS-EEG 扰乱该网络（目标 2），通过以下方式优化记忆编码 最大限度地减少内源性阿尔法振荡。最后，我们将通过功能磁共振成像和脑电图收集的信息整合到 一个单一的计算框架来模拟全球大脑网络的时空动力学， 考虑连通性和脑血管病理学对预测成功的影响 我们的 MCI 队列中的 TMS 相关反应（目标 3）。总之，该项目将使用尖端的大脑刺激和 网络建模技术可增强健康老年人和 MCI 的 WM，并将提供演示 闭环、网络引导的 TMS 对于未来临床应用的价值。

项目成果

Intermittent Theta-Burst Stimulation for Memory Modulation in a Patient With Mild Cognitive Impairment and Trigeminal Neuralgia.

间歇性 Theta 突发刺激对轻度认知障碍和三叉神经痛患者的记忆调节。

• DOI: 10.1097/yct.0000000000000946
• 发表时间: 2023
• 期刊: The journal of ECT
• 作者: McAllister,MargaretL;Slayton,MatthewA;Bukhari-Parlakturk,Noreen;Liu,AndyJ;Peterchev,AngelV;Davis,SimonW
• 通讯作者: Davis,SimonW