Toward a neuroscientific understanding of the interaction between Down syndrome and Alzheimer's disease pathology

对唐氏综合症和阿尔茨海默病病理学之间相互作用的神经科学理解

• 批准号: 10723666
• 负责人: JENNIFER L BRUNO
• 金额: $ 12.58万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723666
• 关键词: Adult; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease pathology; Alzheimer’s disease biomarker; Amyloid; Biological Markers; Brain; Brain Pathology; Brain region; Clinical; Cognitive; Computing Methodologies; Corpus striatum structure; Data; Dementia; Development; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease Progression; Down Syndrome; Enrollment; Functional Magnetic Resonance Imaging; General Population; Goals; Hippocampus; Individual; Intervention; K-Series Research Career Programs; Knowledge; Light; Liquid substance; Longevity; Magnetic Resonance Imaging; Measures; Memory; Modeling; Nerve Degeneration; Neuropsychology; Neurosciences; Outcome; Pathologic; Pathology; Patients; Pattern; Persons; Phenotype; Plasma; Population; Positron-Emission Tomography; Preventive treatment; Property; Research Personnel; Rest; Spectrum Analysis; Structure; Symptoms; Testing; Training; Translational Research; Validation; Work; biophysical properties; brain tissue; career; clinical translation; cohort; connectome; connectome based predictive modeling; design; executive function; follow-up; in vivo; innovation; insight; lifetime risk; mild cognitive impairment; multimodal neuroimaging; multimodality; neurofilament; neuroimaging; neuropathology; neuropsychiatry; novel; predictive marker; skills; social cognition; tau Proteins; therapy development; vector

This application will combine the neuroimaging expertise of the investigator with new knowledge of biomarkers for aging pathology, advanced computational methods and Down syndrome clinical translational research. The goal is to produce a unique skill set which will be used to advance the candidate’s career as an independent lifespan developmental cognitive neuroscientist focusing on Down syndrome. Neurodegenerative changes associated with Alzheimer's disease (AD) are present in almost all individuals with Down syndrome by age 40 years, and the lifetime risk of developing dementia is more than 90% in this population. Understanding patterns of neuropsychiatric and pathological brain changes before AD diagnosis is critical in order to facilitate interventions prior to onset of irreversible neuropsychiatric and pathological brain changes. Yet charting of pathological brain changes and diagnosis of dementia is extremely challenging in individuals with DS who have brain and neuropsychological differences present through the lifespan. Here we propose a computational neuroscientific framework in which we will utilize conventional resting state functional MRI, advanced quantitative MRI and multimodal biomarker data to disentangle brain and neuropsychological changes specific to DS and AD (within the context of DS). Specifically, we will test the overarching hypothesis that whole brain connectivity (i.e. connectomes based on resting state functional MRI) and brain microstructure (quantitative MRI) represent intermediate phenotypes between primary brain pathologies and neuropsychological outcomes in DS and AD. First, we will leverage data from the Alzheimer's Biomarkers Consortium — Down Syndrome (ABC-DS) to identify connectome-wide signatures specific to DS, and a separate set of signatures specific to AD in the context of DS. Then, we will examine relationships between connectome-wide signatures and AD pathology within the amyloid, tau and neurodegeneration (ATN) framework. Next, we will examine relationships between connectome-wide signatures and critical neuropsychological functions including memory, social cognition, and executive function dimensionally. Finally, we will enroll a novel cohort of adults with DS and collect complementary advanced MRI studies (not available in ABC-DS) and fluid biomarkers, to examine brain microstructural properties which may be more a) sensitive to neurodegenerative changes when compared to traditional MRI and b) complementary to PET imaging. Together, the results of this proposal will advance a mechanistic understanding of DS- and AD-specific pathological brain changes and how reorganization of functional networks relate to neuropsychological changes. Further, these data form the basis for a follow-up R01 combining ABC-DS emerging longitudinal data with advanced complementary MRI and multimodal biomarkers.

该应用程序将将研究人员的神经影像学专家与生物标志物的新知识相结合 用于老化病理学，高级计算方法和唐氏综合症临床翻译研究。这 目标是生产独特的技能，该技能将用于推动候选人作为独立的职业 寿命发展的认知神经科学家专注于唐氏综合症。神经退行性变化 与阿尔茨海默氏病有关（AD）几乎所有患有唐氏综合症的人都存在于40岁 多年，在这一人群中，痴呆症的终生风险超过90％。理解模式 在AD诊断之前，神经精神病学和病理大脑的变化至关重要，为了促进 不可逆的神经精神病学和病理大脑变化之前的干预措施。但图表的图表 对于患有DS的个体的病理大脑变化和痴呆症的诊断非常具有挑战性 大脑和神经心理学差异在整个生命周期中存在。在这里，我们提出了一个计算 神经科学框架，我们将利用常规的静止状态功能MRI，高级 定量MRI和多模式生物标志物数据，以消除大脑和神经心理学变化特定的变化 到DS和AD（在DS的背景下）。具体来说，我们将测试整个大脑的总体假设 连接性（即基于静息状态功能MRI的连接组）和脑微观结构（定量 MRI）代表原发性脑病理和神经心理学结果之间的中间表型 在DS和广告中。首先，我们将利用阿尔茨海默氏症生物标志物联盟的数据 - 唐氏综合症 （ABC-DS）识别DS特有的connectome范围签名以及特定于 在DS的背景下进行广告。然后，我们将研究范围内签名与广告之间的关系 淀粉样蛋白，TAU和神经变性（ATN）框架内的病理。接下来，我们将检查关系 在全连接组的签名和关键的神经心理学功能之间，包括记忆，社交 认知和执行功能在维度上。最后，我们将招募一个新颖的成年人与DS和 收集完整的高级MRI研究（ABC-DS中不可用）和流体生物标志物，以检查大脑 相比 传统MRI和B）宠物成像的完整性。该提案的结果在一起将推进 对DS和AD特异性病理大脑变化以及如何重组的机械理解 功能网络与神经心理学变化有关。此外，这些数据构成了随访的基础 R01将ABC-DS与高级完成MRI和多模式相结合 生物标志物。