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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos 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

项目摘要

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多项临床试验显示轻度认知障碍，但尚无有效的药物治疗。损伤（MCI）是 AD 的先兆风险，并且在临床前研究中更容易进行干预。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 疗效可靠，我们提出了缺乏疗效的三个原因。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其次，标准 TMS 协议无法捕捉认知的动态本质。通过了解内源性大脑状态对外源性神经调节的反应。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 第三，没有研究在 AD 相关人群中使用 TMS。我们建议解决脑血管疾病对 TMS 反应的影响。通过使用闭环TMS，基于个性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 性能的影响这种方法建立在我们现有的 K01、U01 和 RF1 奖项的基础上。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，我们应用新型闭环 TMS。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其中。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。