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

项目概要/摘要 虽然西方世界的艾滋病毒感染者的预期寿命接近一般人口， 他们患HIV相关神经认知障碍（HAND）的风险显著升高。等 神经系统疾病是艾滋病最常见的神经系统并发症， 35-70%的HIV感染者，针对此类合并症的研究已被确定为 艾滋病研究办公室的首要任务（NOT-OD-15-137）。虽然HAND背后的机制是 虽然还不太清楚，但许多人类神经影像学研究已经检查了可能与大脑相关的大脑区域。 参与，总的来说，这些研究在确定关键枢纽和大规模 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， 因此，存在进一步研究关键生理参数的明确和共同的需要，这些生理参数 可能是HAND的发展和进展的基础，特别是在衰老的背景下。 众所周知，人类皮层神经元在缺乏外来刺激的情况下表现出自发放电， 和内源性输入。在整个神经元群体中，这些放电与局部树突电流相加 和突触电位以产生皮质节律活动，其通常被称为“自发活动”。 这种自发节律在整个人类大脑中无处不在，但它们在调节认知中的作用是不确定的。 才刚刚开始被理解。最近，我们使用脑磁图（MEG）显示， 的自发活动直接影响神经振荡活动在同一皮质区，并反过来调制 对照组和HIV感染成人的真实的时间任务表现。此外，我们已经表明，自发 多个大脑区域的活动随着年龄的增长而增加，这种增加对表现有害， 并且这些作用在患有HAND的患者中加重。事实上，我们的初步数据表明， 特定脑区的自发活动水平可以区分年龄匹配的HIV感染患者， 没有手。在这个项目中，我们将量化自发性的年龄相关升高的轨迹， 大脑皮层活动在一个大组的艾滋病毒感染的成年人和人口统计学匹配的控制。然后我们将 评估这种自发活动的强度如何影响局部神经振荡和真实的时间认知 性能基于广泛的初步数据，我们假设自发活动将随着时间的推移而增加。 年龄在两组中，并且这种增加的斜率在HIV感染的成年人中相对于对照组更陡， 在那些有手的人中，重要的是，我们还假设这些增加将是 与认知基础神经振荡强度的成比例变化相关， 行为性能指标，如准确性和反应时间。最后，在目标3中，我们将利用深度 学习方法，根据神经生理学指标将我们的样本分为不同的组， MEG，黄金标准是他们的整体神经心理学特征。

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.