THE PROGRESSIVE ELEVATION OF SPONTANEOUS CORTICAL ACTIVITY IN HAND (PESCAH) PROJECT

项目摘要/摘要 尽管西方世界感染艾滋病毒的患者的预期寿命接近普通人群， 他们患艾滋病毒相关神经认知障碍(HAND)的风险显著增加。是这样的 疾病是HIV疾病最常见的神经系统并发症，患病率估计各不相同 来自35%-70%的艾滋病毒感染患者，针对此类并存的研究已被确定为 艾滋病研究办公室的首要任务(非-OD-15-137)。而作为手基础的机制是 尚不清楚的是，许多人类神经成像研究已经检查了可能是 总的来说，这些研究在很大程度上成功地确定了关键枢纽和大规模 网络。然而，关于这些大脑区域内的基本回路功能障碍仍然存在许多问题，并且 因此，有一个明确和普遍的需要，进一步研究关键的生理参数， 可能是手的发育和进展的基础，特别是在衰老的背景下。 众所周知，人的大脑皮层神经元在没有外源性传入的情况下表现出自发放电。 和内生投入。在整个神经元群体中，这些放电与局部树突电流相加。 以及产生皮层节律性活动的突触电位，这通常被称为“自发活动”。 这种自发的节奏在整个人类大脑中无处不在，但它们在调节认知方面的作用是 只是刚刚开始被理解。最近，我们用脑磁图(MEG)显示了 自发活动的大小直接影响同一皮质区域的神经振荡活动，进而调节 控制组和感染艾滋病毒的成年人的实时任务表现。此外，我们已经证明了自发的 多个大脑区域的活动随着年龄的增长而增加，这种增加对表现是有害的， 而这些影响在手部疾病患者中更加明显。事实上，我们的初步数据表明， 特定脑区自发活动水平可区分年龄匹配的HIV感染者和 而且没有手。在这个项目中，我们将量化与年龄相关的自发上升的轨迹 在一大群感染艾滋病毒的成年人和人口统计学上匹配的对照组中，大脑皮质活动。到时候我们会的 评估这种自发活动的强度如何影响局部神经振荡和实时认知 性能。基于大量的初步数据，我们假设自发活动将随着 这两组人的年龄，这种增加的斜率在感染艾滋病毒的成年人中将比对照组更陡峭， 而且在那些用手的人身上更加突出。重要的是，我们还假设这些增长将是 与认知基础神经振荡强度的比例变化有关，以及 行为表现指标，如准确性和反应时间。最后，在目标3中，我们将利用深度 基于神经生理学指标对样本进行分组的学习方法 MEG，以他们的整体神经心理档案为黄金标准。

Project Summary/Abstract Although HIV-infected patients in the western world have a life expectancy near that of the general population, they are at a significantly elevated risk of developing HIV-associated neurocognitive disorders (HAND). Such disorders are the most common neurological complication of HIV disease, with prevalence estimates ranging from 35-70% of all HIV-infected patients, and research targeting such comorbidities has been identified as a top priority by the Office of AIDS Research (NOT-OD-15-137). While the mechanisms that underlie HAND are not well understood, numerous human neuroimaging studies have examined the brain regions that may be involved, and overall these studies have been largely successful in identifying the critical hubs and large-scale networks. However, many questions remain regarding basic circuit dysfunction within these brain regions, and consequently there is a clear and common need to further investigate the key physiological parameters that may underlie the development and progression of HAND, especially in the context of aging. It is well known that human cortical neurons exhibit spontaneous firing in the absence of incoming exogenous and endogenous input. Across a neuronal population, these discharges summate with local dendritic currents and synaptic potentials to produce cortical rhythmic activity, which is often referred to as “spontaneous activity.” Such spontaneous rhythms are ubiquitous throughout the human brain, but their role in modulating cognition is only beginning to be understood. Recently, we used magnetoencephalography (MEG) to show that the strength of spontaneous activity directly affects neural oscillatory activity in the same cortical area, and in-turn modulates real time task performance in controls and HIV-infected adults. Additionally, we have shown that spontaneous activity in multiple brain regions increases with advancing age, that this increase is deleterious to performance, and that these effects are accentuated in patients with HAND. In fact, our preliminary data suggests that the level of spontaneous activity in specific brain regions may distinguish age-matched HIV-infected patients with and without HAND. In this project, we will quantify the trajectory of age-related elevations in spontaneous cortical activity in a large group of HIV-infected adults and demographically-matched controls. We will then evaluate how the strength of such spontaneous activity affects local neural oscillations and real time cognitive performance. Based on extensive preliminary data, we hypothesize that spontaneous activity will increase with age in both groups, and that the slope of this increase will be steeper in HIV-infected adults relative to controls, and further accentuated in those with HAND. Importantly, we also hypothesize that these increases will be associated with proportional changes in the strength of the neural oscillations underlying cognition, and behavioral performance indices such as accuracy and reaction time. Finally, in Aim 3, we will utilize deep learning methods to classify our sample into groups based on the neurophysiological metrics derived from MEG, with the gold standard being their overall neuropsychological profile.