Anatomic mechanisms of resilience and genetic susceptibility in TDP-related disorders

项目摘要 行为变异性额颞叶变性（bvFTD）是一种常见的原因， 神经退行性疾病和预期寿命约为7年，但这是高度可变的。最近 这项研究将认知和MRI成像的变化与FTLD-TDP累积的病理阶段联系起来， FTLD-Tau病理学。然而，知识方面的主要差距涉及到死亡率的巨大变化， bvFTD的临床进展，以及导致这种变异性的因素。研究已经证明 生活方式因素可减缓神经退行性疾病的临床进展速度。这归因于 认知储备，一种弹性的形式，认知策略有助于支持大脑在面对 无情地积累病理，从而调节纵向下降的速度。比如说， 神经解剖学因素也可能在代偿功能的神经实现中起作用，例如 支持交替的大脑网络以获得最佳性能。与单核苷酸相关的遗传因素 多态性（SNP）也可能通过选择性增加bvFTD的下降速率， 解剖网络对疾病负担的脆弱性本建议的总体目标是更好地了解如何 生活方式和遗传因素影响神经网络以影响纵向下降的速度。目标1： 将研究教育，职业和休闲活动，减缓纵向下降的速度， 认知和功能测量。我们将其与灰质（GM）和白色物质（WM）的MRI相关， 强大的图论分析，检查网络连接的关键指标，并阐明 恢复力的神经解剖学基础AIM 2将使用动脉自旋标记来增强网络模型 连接，预测纵向临床下降的速度，并期望找到关键的连接特性 与脑血流相关的额叶网络，有助于bvFTD的恢复。在目标3中，我们 检查假设驱动的SNP，这些SNP可能会减缓临床下降的速度，并将SNP变异与 连接网络中的解剖区域。这种独特的生活方式、遗传和成像因素的结合 将产生对临床管理至关重要的临床疾病进展的新型预测模型， 临床试验终点，同时为患者和家属提供重要的预后数据。 !

Project Summary Behavioral variant frontotemporal degeneration (bvFTD) is a common cause of young-onset neurodegenerative disease and life expectancy is approximately 7 years, but this is highly variable. Recent work associates change in cognitive and MRI imaging with pathologic phases of accumulating FTLD-TDP and FTLD-Tau pathology. However, major gaps in knowledge concern the tremendous variability in the rate of clinical progression in bvFTD, and the factors contributing to this variability. Studies have demonstrated that lifestyle factors moderate the rate of clinical progression in neurodegenerative disease. This is attributed to cognitive reserve, a form of resilience where cognitive strategies help support brain functioning in the face of relentlessly accumulating pathology and thus modulate the rate of longitudinal decline. For example, neuroanatomic factors may also play a role in neural implementation of compensatory function, such as supporting alternate brain networks for optimal performance. Genetic factors associated with single nucleotide polymorphisms (SNPs) also may impact the variable rate of decline in bvFTD by selectively increasing anatomic network vulnerability to disease burden. The overall aim of this proposal is to better understand how lifestyle and genetic factors impact neural networks to influence the rate of longitudinal decline. In Aim 1, we will examine education, occupation and leisure activities that moderate the rate of longitudinal decline on cognitive and functional measures. We will relate this to MRI of gray matter (GM) and white matter (WM) using powerful graph theoretic analyses that examine key metrics of network connectivity and elucidate the neuroanatomic basis of resilience. Aim 2 will use arterial spin labeling to enhance models of network connectivity that predict the rate of longitudinal clinical decline, and expect to find key connectomic properties of frontal networks related to cerebral blood flow that contribute to resilience in bvFTD. In Aim 3, we will examine hypothesis-driven SNPs that may moderate the rate of clinical decline and relate SNP variation to anatomic regions in connectomic networks. This unique combination of lifestyle, genetic and imaging factors will lead to novel predictive models of clinical disease progression that are critical for clinical management and clinical trial endpoints, while providing important prognostic data for patients and families. !