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: 10526714
• 负责人: Simon W Davis
• 金额: $ 73.35万
• 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/10526714
• 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 Progression; Dose; Elderly; Electroencephalography; Failure; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Human; Impairment; Individual; Intervention; Lead; Link; 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; Sum; Syndrome; System; Techniques; Time; Transcranial magnetic stimulation; Work; base; brain health; care costs; cerebrovascular pathology; clinical application; cognitive ability; cognitive function; cognitive performance; cohort; combat; 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 circuit; neurodegenerative dementia; neurophysiology; neuroregulation; neurovascular; noninvasive brain stimulation; novel; preclinical study; recruit; relating to nervous system; 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.

项目摘要 由于阿尔茨海默氏病（AD）引起的痴呆症是美国领先的公共卫生问题 而且没有有效的药物疗法任务多个临床试验。多项研究表明了轻度认知 损害（MCI）是AD的前体风险，并且更适合干预。而临床前研究 已经表明，使用非侵入性脑刺激直接调节前额叶皮层（PFC）的活性 诸如经颅磁刺激（TMS）之类的技术可以调节健康较老的认知功能 成人，很少有证据表明MCI可靠效率。我们缺乏效率的三个原因。第一的， 没有建立的方法可以估算可靠的生物标志物以及独特的剂量响应关系 在TMS强度和大脑活动之间，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协议无法捕获认知的动态性质 状态和内源性大脑对外源性神经调节的反应。通过理解 与成功的大脑状态相关的动态变化，应该可以动态操作PFC 以增强认知的方式。第三，在与广告相关的人群中使用TMS的研究没有考虑到 脑血管疾病在对TMS反应中的影响。我们建议解决这些缺点 通过使用闭环TMS，基于个性化的大脑网络来建立可以可靠的参数 在健康衰老和MCI中，在正常内存功能过程中控制大脑状态。 为了实现这一目标，我们将研究网络基础的网络激活和神经振荡机制 调节工作记忆（WM），这是一种具有可靠的基于PFC的网络表征的认知功能。 然后，我们将使用闭环TMS将该网络靶向PFC，并测量对WM性能的影响 和基于任务的神经活动。这种方法是基于我们现有的K01，U01和RF1奖的这种方法 并发TMS-FMRI识别工作记忆网络中的剂量反应关系，可以使用 识别神经塑性并优化TMS的靶向（AIM 1）。接下来，我们将新颖的闭环TMS应用于 使用暂时专门的TMS-EEG（AIM 2）扰动该网络，通过 最小化内源性α振荡。最后，我们将通过fMRI和EEG收集的信息整合到 一个单一的计算框架，以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的影响 我们的MCI队列中与TMS相关的响应（AIM 3）。总而言之，该项目将使用尖端的大脑刺激和 在健康的老年人和MCI中增强WM的网络建模技术，并将提供演示 闭环，网络指导的TMS的价值用于将来的临床应用。