Cognitive and Molecular Challenges to Statistical Inference Across Healthy Aging.

健康老龄化过程中统计推断的认知和分子挑战。

基本信息

批准号：
10005106
负责人：
Matthew Nassar
金额：
$ 24.8万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10005106
关键词：
Address Affect Age Age-associated memory impairment Aging Arousal Award Bayesian learning Behavior Behavioral Belief Biological Biology Brain Brain Stem Cognitive Cognitive aging Comparative Study Data Decision Making Diagnostic Dopamine Elderly Electroencephalography Foundations Glutamates Goals Human Hybrids Impairment Individual Differences Intervention Joints Laboratory Research Lead Learning Life Link Longevity Magnetic Resonance Spectroscopy Maps Measurable Measurement Measures Mediating Mediator of activation protein Memory Memory impairment Molecular Nature Neural Network Simulation Neurodegenerative Disorders Norepinephrine Perception Performance Phase Play Process Property Proxy Psychological Theory Recording of previous events Reporting Research Research Personnel Research Support Risk Role Schools Short-Term Memory Signal Transduction Source Stimulus System Task Performances Techniques Testing Time Training Uncertainty Visual Work age related base behavior measurement behavioral study career cognitive function comparative emerging adult expectation experimental study gamma-Aminobutyric Acid glutamatergic signaling healthy aging improved information processing lens neural network psychobiology psychologic relating to nervous system skills theories tool trend visual information young adult

项目摘要

Age-related cognitive decline is a problem of growing importance given the trend toward increased lifespan and the importance of cognitive function in determining risk for neurodegenerative disease. Despite the importance of this problem, the cognitive and molecular changes that mediate it are still poorly understood. While there are competing theories for the mediators of cognitive aging at both psychological and molecular levels, theories on aging and the research supporting them have typically focused on a single level of analysis. As our understanding of the biological basis for behavior grows, this divide becomes less sensible. Instead, psychological theory should be constrained according to its known biology, and biology should in turn inform psychological theory. Here I propose to learn neural network modeling and magnetic resonance spectroscopy (MRS) techniques in order to bridge human psychological theory that has been the focus of my recent work to the molecular level theory that was the focus of my post-baccalaureate training at the NIA. I will build from my recent work that highlights a key statistical problem faced by the brain: how to selectively pool information across relevant sources but partition information across irrelevant ones. I will closely examine the cognitive and molecular mechanisms that allow for efficient pooling and partitioning to test the overarching theory that, due to impaired glutamate and dopamine signaling, older adults develop a selective deficit in pooling relevant sources of information. I will test this theory in two separate paradigms: visual working memory (K99) and learning and perceptual inference (R00). During the K99 phase of the award I will examine how pooling information from visual targets with similar features can 1) improve effective memory capacity, 2) be achieved by a neural network model, and 3) be impaired by simulated molecular deficiencies (glutamate, dopamine, norepinephrine). This bridge between cognitive and molecular levels of analysis will be used to test whether age-related memory deficits are due to inefficient pooling and mediated by molecular deficits in glutamate and dopamine (as measured through MRS and behavioral proxy, respectively). During the R00 phase of the award I will use the training in neural networks and MRS provided in the K99 phase to examine how 1) pooling sequential pieces of information affects learning and perceptual bias, 2) efficient pooling and partitioning can be achieved by a neural network model and 3) these processes are disrupted by specific molecular deficiencies. The established relationships between statistical properties (sequential pooling and partitioning), psychological measurements (learning and perceptual bias) and molecular factors (glutamate, dopamine, and norepinephrine signaling levels) will be used to test whether age-related differences in learning and perceptual bias reflect deficient pooling mediated by local glutamate deficiency (as measured through MRS). Overall, this work will contribute a deeper and more detailed understanding of the psychological and molecular factors that interact to mediate age-related cognitive decline. In addition, the K99 training will provide me the tools necessary to link psychological and molecular levels of analysis and the R00 phase will build the foundations of my independent research laboratory allowing me to develop a successful scientific career driven by experiments that build and refine a unified understanding of cognitive aging.

鉴于寿命增加趋势，与年龄有关的认知下降是一个越来越重要的问题认知功能在确定神经退行性疾病风险中的重要性。尽管很重要问题，介导其的认知和分子变化仍然很少理解。虽然有竞争在心理和分子水平上的认知衰老介体的理论，衰老和衰老理论支持他们的研究通常专注于单个分析。作为我们对生物基础的理解对于行为的增长，这种鸿沟变得不太明智。相反，心理理论应根据其已知的生物学和生物学应该依次为心理理论提供依据。在这里，我建议学习神经网络建模和磁共振光谱（MRS）技术，以弥合人类心理理论我最近的工作重点放在分子水平理论上，这是我在尼亚州核后训练的重点。我将从我最近的工作中建立，该工作突出了大脑面临的关键统计问题：如何有选择地汇总跨相关来源的信息，但分区信息跨越无关。我将仔细检查认知和分子机制，允许有效的合并和分区来测试总体理论，该理论由于谷氨酸和多巴胺信号受损，老年人在合并相关来源时出现选择性赤字信息。我将在两个单独的范式中测试该理论：视觉工作记忆（K99）和学习和感知推理（R00）。在奖励的K99阶段，我将研究如何通过视觉目标汇总信息类似功能可以1）提高有效的记忆能力，2）通过神经网络模型实现，3）受到损害通过模拟分子缺陷（谷氨酸，多巴胺，去甲肾上腺素）。认知和分子之间的这座桥分析水平将用于测试与年龄相关的记忆缺陷是否是由于合并效率低下而引起的谷氨酸和多巴胺的分子缺陷（分别通过MRS和行为代理测量）。在奖项的R00阶段我将使用K99阶段提供的神经网络和MRS的培训来检查1）汇总顺序信息会影响学习和感知偏见，2）有效的合并和分区可以是通过神经网络模型和3）这些过程被特定的分子缺陷所破坏。这统计特性（顺序汇总和分区）之间建立的关系，心理测量（学习和感知偏差）和分子因子（谷氨酸，多巴胺和去甲肾上腺素信号传导水平）将是用于测试与年龄相关的学习和感知偏见的差异反映了当地介导的不足的合并谷氨酸缺乏症（通过MRS测量）。总体而言，这项工作将贡献更深入，更详细的了解与年龄相关的认知下降相互作用的心理和分子因素。在此外，K99培训将为我提供链接心理和分子分析水平的必要工具， R00阶段将建立我的独立研究实验室的基础，使我能够开发成功科学职业是由实验驱动的，这些实验会建立和完善对认知衰老的统一理解。