High-resolution multimodal imaging of episodic memory networks in aging.

描述(由申请者提供)：在这个独立之路奖中提出的培训和研究计划将推动候选人成为研究型大学终身教职的独立科学家。这一奖项将支持她对有关神经网络结构连通性的新问题的研究，神经网络结构连通性有助于整个生命周期的情节记忆。她将被介绍到高分辨率多模式神经成像，并将接受相应的高级核磁共振成像和多变量分析技术的培训。该提案将重点放在情景记忆的组成部分过程(即模式分离)、支持这些过程的神经网络以及它们在健康衰老和痴呆症风险增加的个人中如何受到影响，这也将加强候选人在神经认知老化研究方面的专业知识。重要的是，这一奖项将为候选人在职业生涯的早期阶段提交一份重要的研究提案(例如，R01)做好准备，否则这是可能的。环境加州大学欧文分校(UCI)提供了一系列独特的培训和发展资源，帮助候选人晋升为独立科学家。这包括一个由学习和记忆神经生物学中心(CNLM)的杰出研究人员组成的合作小组，他们致力于了解支持记忆的神经机制，一个杰出的导师(CNLM主任Craig Stark博士)和共同导师(UCI记忆受损和神经疾病研究所临床核心主任Claudia Kawas博士)，他们的开创性研究计划为当前的提案奠定了基础，提供最先进的研究和神经成像设施，以及各种将在整个奖项有效期内促进教育和职业发展的课程和研讨会。研究。目前这项提议的中心目标是利用行为和高分辨率多模式神经成像技术，研究贯穿一生的模式分离神经网络，这是情节记忆的一个组成部分过程。间歇性记忆衰退是健康衰老和与年龄相关的认知障碍的标志特征，如健忘性轻度认知障碍和阿尔茨海默病。进一步详述支持情景记忆成分过程的神经机制可能有助于识别与认知衰老相关的神经标记物，并为旨在促进成功衰老的认知和神经干预提供信息。情节记忆是一种复杂的助记能力，涉及对离散事件的编码和检索，包括事件发生的细节，如事件发生的内容、地点和时间。对新信息的成功编码需要将类似的事件分成不同的记忆表示法。这一过程被称为模式分离，已知依赖于内侧颞叶(MTL)亚区。然而，神经成像研究表明，前额叶皮质(PFC)和纹状体也参与了情景记忆的执行。虽然这些分布的大脑区域经常被孤立地研究，但全面了解情节记忆的神经基础，特别是模式分离，将需要了解它们作为相互连接的神经网络是如何相互作用的。在该奖项的指导阶段，将使用高分辨率扩散张量成像(DTI)和功能磁共振成像(FMRI)来识别比之前在活体中获得的更准确的MTL(特定目标1)和纹状体(特定目标2)连接模型。在我们早期对穿孔通路的研究基础上，这项拟议的研究将评估连接MTL亚区的局部束(例如穿孔通路、苔藓纤维和Schaffer侧支)和连接MTL和PFC的大规模束(例如穹隆、扣带)对健康成年人模式分离性能的贡献。它也将是第一个检查 连接纹状体和前额叶的束(如尾状核-前额叶、壳核-运动)的完整性与这些助记过程有关。该奖项的独立阶段将评估MTL和纹状体记忆系统之间的相互作用和分离，这两个系统通常被认为受到健康和病理性衰老的不同影响。我们将测试以下假设，即纹状体相对于MTL束完整性的退化导致健康老年人相对于年轻成年人的模式分离下降(特定目标2)，以及MTL相对于纹状体连通性的退化导致老年与年轻老年人的模式分离下降(特定目标3)。这些数据将直接测试皮质连接中断理论，该理论认为白质连接减弱是与衰老相关的认知衰退的原因。

DESCRIPTION (provided by applicant): The training and research plan proposed in this Pathway to Independence Award will propel the candidate to become an independent scientist in a tenure-track position at a research university. This award will support her investigation of novel questions regarding structural connectivity of neural networks that subserve episodic memory across the lifespan. She will be introduced to high-resolution multimodal neuroimaging and will receive training in corresponding advanced MRI and multivariate analysis techniques. The candidate's expertise in neurocognitive aging research will also be strengthened by the proposal's focus on component processes of episodic memory (i.e., pattern separation), the neural networks that support these processes, and how they are affected in both healthy aging and in individuals at increased risk for dementia. Importantly, this award will prepare the candidate to submit a major research proposal (e.g., R01) at an earlier stage in her career that would be possible otherwise. Environment. The University of California, Irvine (UCI) offers a unique array of training and development resources to facilitate the candidate advancing to an independent scientist position. These include a collaborative group of distinguished researchers at the Center for the Neurobiology of Learning and Memory (CNLM) dedicated to understanding neural mechanisms that support memory, an exceptional mentor (Dr. Craig Stark, Director of the CNLM) and co-mentor (Dr. Claudia Kawas, Clinical Core director of the UCI Institute for Memory Impairments and Neurological Disorders) whose pioneering research programs laid the foundation for the current proposal, access to state-of-the-art research and neuroimaging facilities, and a variety of courses and workshops that will accelerate both educational and career development throughout the duration of this award. Research. The central aim of the current proposal is to investigate neural networks of pattern separation, a component process of episodic memory, across the life span using behavioral and high- resolution multimodal neuroimaging techniques. Episodic memory decline is a hallmark feature of healthy aging and age-related cognitive disorders such as amnestic mild cognitive impairment and Alzheimer's disease. Further detailing the neural mechanisms that support component processes of episodic memory may facilitate identification of neural markers associated with cognitive aging, and inform cognitive and neural interventions aimed at promoting successful aging. Episodic memory is a complex mnemonic ability that involves encoding and retrieval of discrete events, including details such as what, where, and when an event occurred. Successful encoding of new information requires that similar events get separated into distinct memory representations. This process, termed pattern separation, is known to rely on medial temporal lobe (MTL) subregions. However, neuroimaging studies have shown that prefrontal cortex (PFC) and striatum are also engaged during episodic memory performance. Whereas these distributed brain regions are frequently studied in isolation, a comprehensive understanding of the neural substrates of episodic memory in general, and pattern separation in particular, will require knowledge of how they interact as interconnected neural networks. In the mentored phase of this award, high-resolution diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) will be used to identify more accurate models of MTL (Specific Aim 1) and striatal (Specific Aim 2) connectivity across the human lifespan than has been previously acquired in vivo. Expanding on our earlier work with the perforate path, the proposed study will assess the contribution of local tracts connecting MTL subregions (e.g., perforate path, mossy fibers, and schaffer collaterals) and large-scale tracts connecting MTL to PFC (e.g., fornix, cingulum) to pattern separation performance in healthy adults. It will also be the first to examine integrity of tracts connecting striatum to PFC (e.g., caudate-PFC, putamen-motor) in relation to these mnemonic processes. The independent phase of this award will assess interactions and dissociations between MTL and striatal memory systems, which are frequently regarded as being differentially affected by healthy and pathological aging. We will test the hypotheses that degradation of striatal versus MTL tract integrity accounts for pattern separation declines in healthy older versus younger adults (Specific Aim 2) and that degradation of MTL versus striatal tract connectivity accounts for pattern separation declines in oldest- old versus younger-old adults (Specific Aim 3). These data will directly test cortical disconnection theories, which propose that diminished white matter connectivity accounts for cognitive declines associated with aging.