Treating primary progressive aphasia and elucidating neurodegeneration in the language network using transcranial direct current stimulation

项目摘要/摘要 此修订后的应用程序响应PAR-18-175：“阿尔茨海默氏病频谱的试点临床试验 以及与年龄相关的认知功能下降。原发性进行性失语症(PPA)，一种削弱语言能力的疾病 影响许多额颞叶痴呆(FTD)和阿尔茨海默病(AD)患者的丢失，目前缺乏 有效的治疗方法。最近的研究表明，经颅直流电刺激(Tdcs)是一种 非侵入性神经调节，有望成为PPA的一种干预手段。然而，这些研究努力 是因为它们没有解决关于语言系统可塑性的重要问题，以及 因为他们没有充分利用关于PPA中语言网络的属性的知识来指导 治疗。这项提议旨在进一步推进对tdcs的调查，作为对PPA和 为了确定PPA中语言网络的哪些组成部分最有能力产生tdcs诱导的行为 相关的可塑性。我们的建议旨在确定神经调节疗法是否适用于PPA患者 应该致力于加强语言网络中最退化地区的联系或支持 更完整地区的补偿性变化。我们将通过追求一种随机的、 左侧前部高密度TDCs(HD-TDCs)的假控制交叉研究 两种PPA变体受试者的大脑半球语言网络，其特点是词量减少 生产，但以不同的最大退化部位为特征。这将允许比较 在一些受试者中退化，但在其他受试者中相对较少的区域的刺激。刺激将是 配合旨在增强语言系统中tdcs效应的行为语言疗法。我们的第一次 目的是确定这种干预如何不同地影响患有 两个PPA变种。然后，我们将使用神经成像数据的网络图统计分析来表征 语言网络。我们将重点关注集线器作为网络中关键连接的中心，我们建议 衡量语言网络中各区域发挥枢纽作用的能力的变化(按枢纽编制索引 分数)可能是描述神经变性如何影响PPA中语言网络功能的一种方式。因此， 该提案的第二个目标将探索在基线水平上整个语言网络中心分数的差异 在我们的两个PPA亚型组中。提案的第三个目标将通过审查来扩展这一方法 Tdcs引起的HUB评分的行为相关变化。该项目的最终目标将整合和 通过开发一种使用临床表型、脑萎缩模式和 中心得分数据，以预测哪些个人最有可能从我们的刺激方法中受益。已被占用 总之，该项目将推进对与以下疾病相关的破坏性疾病的潜在干预 神经退行性疾病，阐明这些疾病中可塑性的网络机制，并发展出一种 预测治疗神经调节反应的潜在的可推广的、网络信息的方法。

PROJECT SUMMARY/ABSTRACT This revised application responds to PAR-18-175: “Pilot Clinical Trials for the Spectrum of Alzheimer's Disease and Age-related Cognitive Decline.” Primary progressive aphasia (PPA), a debilitating condition of language loss affecting many patients with frontotemporal dementia (FTD) and Alzheimer's disease (AD), currently lacks effective treatments. Recent studies suggest that transcranial direct current stimulation (tDCS), a form of noninvasive neuromodulation, may show promise as an intervention for PPA. However, these research efforts are hampered because they do not address important questions about plasticity in the language system, and because they do not fully utilize knowledge regarding the properties of the language network in PPA to guide treatment. This proposal aims to further advance investigation into tDCS as a potential intervention in PPA and to establish which components of the language network in PPA are most capable of tDCS-induced behaviorally relevant plasticity. Our proposal seeks to determine whether neuromodulation therapies in persons with PPA should aim to strengthen connections in the most degenerated regions of the language network or bolster compensatory changes in more intact areas. We will address this knowledge gap by pursuing a randomized, sham-controlled crossover study of high-density tDCS (HD-tDCS) focused over the anterior regions of the left hemisphere language network in participants with two PPA variants that are characterized by decreased word production but which feature different sites of maximal degeneration. This will allow for comparison of stimulation in a region that is degenerated in some subjects but relatively spared in others. Stimulation will be paired with a behavioral language therapy aimed at augmenting tDCS effects in the language system. Our first aim will be to determine how this intervention differentially impacts language performance in subjects with the two PPA variants. We will then use network graph statistical analyses of neuroimaging data to characterize language networks. We will focus on hubs as centers of critical connectivity in networks, and we propose that measuring changes in the ability of regions in the language network to function as hubs (indexed by hub scores) may be a way to describe how neurodegeneration impacts language network functions in PPA. Thus, the second aim of the proposal will explore differences in hub scores across the language network at baseline in our two PPA subtype groups. The third aim of the proposal will extend this approach by examining behaviorally relevant changes in hub scores induced by tDCS. The final aim of the project will integrate and extend prior findings by developing a model that employs clinical phenotypes, patterns of brain atrophy, and hub score data to predict which individuals are most likely to benefit from our stimulation approach. Taken together, this project will advance a potential intervention for a devastating condition associated with neurodegenerative diseases, elucidate network mechanisms of plasticity in these disorders, and develop a potentially generalizable, network-informed approach for predicting response to therapeutic neuromodulation.