Adaptive Neuromodulation of Working Memory Networks in Aging and Dementia

项目摘要 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，其护理费用巨大 尽管进行了多项临床试验，但没有有效的药物治疗。多项研究表明， 因此，研究人员认为MCI是AD的前兆风险，更容易干预。虽然临床前研究 已经表明，使用非侵入性脑刺激直接调节前额叶皮层（PFC）的活动， 技术，如经颅磁刺激（TMS），可以调节健康老年人的认知功能， 在成年人中，几乎没有证据表明MCI的可靠疗效。我们认为缺乏功效的原因有三个。第一、 没有确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，标准的TMS协议未能捕捉认知的动态本质， 状态和内源性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，在AD相关人群中使用TMS的研究没有解释 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，基于个性化的大脑网络来建立能够可靠地 在健康老龄化和MCI的正常记忆功能过程中控制大脑状态。 为了实现这一目标，我们将研究网络激活和网络背后的神经振荡机制 它调节工作记忆（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将此网络定位到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K 01、U 01和RF 1奖项的基础上， 并行TMS-fMRI来识别工作记忆网络中的剂量-反应关系， 识别神经可塑性并优化TMS的靶向（目标1）。接下来，我们将新型闭环TMS应用于 使用时间精确的TMS-EEG（目标2）干扰该网络，通过以下方式优化记忆编码： 最小化内源性α振荡。最后，我们将整合通过fMRI和EEG收集的信息， 一个单一的计算框架，以模拟全球大脑网络的时空动态， 考虑到连通性和脑血管病理学在预测治疗成功方面的影响， 我们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， 网络建模技术，以提高健康老年人和MCI的WM，并将提供示范 闭环、网络引导的TMS在未来临床应用中的价值。

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.