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.

该应用将联合收割机结合研究者的神经影像学专业知识和生物标志物的新知识 用于衰老病理学、先进计算方法和唐氏综合征临床转化研究。的 目标是产生一个独特的技能，将用于推进候选人的职业生涯作为一个独立的 终身发展认知神经科学家，专注于唐氏综合症。神经退行性改变 与阿尔茨海默氏病（AD）相关的几乎所有唐氏综合征患者在40岁时都存在 在这一人群中，患痴呆症的终生风险超过90%。理解模式 在AD诊断之前，神经精神和病理性脑变化的评估是至关重要的， 在不可逆的神经精神和病理性脑变化发生之前进行干预。然而， 病理性脑变化和痴呆症的诊断在患有DS的个体中是极其具有挑战性的， 大脑和神经心理学的差异贯穿整个生命周期。在这里，我们提出了一个计算 神经科学框架，我们将利用传统的静息状态功能磁共振成像，先进的 定量MRI和多模式生物标志物数据，以解开大脑和神经心理学变化的特异性 D和AD（在DS的上下文中）。具体来说，我们将测试整个大脑的总体假设， 连接（即基于静息状态功能MRI的连接组）和脑微结构（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AD中。首先，我们将利用阿尔茨海默氏症生物标志物联盟-唐氏综合症的数据， （ABC-DS）来识别特定于DS的连接组范围的签名，以及特定于 在DS的背景下。然后，我们将研究连接组范围的签名和AD之间的关系 淀粉样蛋白、tau蛋白和神经变性（ATN）框架内的病理学。接下来，我们将研究 连接组范围的签名和关键的神经心理功能，包括记忆，社会， 认知和执行功能维度。最后，我们将招募一组新的DS成人队列， 收集补充的高级MRI研究（ABC-DS中不可用）和液体生物标志物，以检查大脑 微结构特性，其可能a）与以下相比，对神经退行性变化更敏感： 传统MRI和B）与PET成像互补。总之，这项建议的结果将推动一个 对DS和AD特异性病理性脑变化的机制性理解以及 功能网络与神经心理学变化有关。此外，这些数据构成了后续行动的基础 R 01结合ABC-DS新兴纵向数据与高级补充MRI和多模式 生物标志物。