Spatiotemporal Patterns of Network Dysfunction in Alzheimer's Disease

阿尔茨海默病网络功能障碍的时空模式

• 批准号: 9051982
• 负责人: Kamalini Gayathree Ranasinghe
• 金额: $ 6.03万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-05 至 2019-03-04
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9051982
• 关键词: Affect; Age; Alzheimer' s Disease; Animal Model; Area; Auditory; Auditory area; Behavioral; Bilateral; Biological Markers; Biological Neural Networks; Brain; Brain region; Clinical; Clinical Trials; Cognition; Cognitive deficits; Communication; Computer software; Dementia; Development Plans; Disease; Doctor of Philosophy; Dorsal; Early Diagnosis; Elderly; Feedback; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Hearing; Human; Image; Impaired cognition; Individual; Knowledge; Language; Language Disorders; Measures; Mentors; Methodology; Methods; Motor; Neurobehavioral Manifestations; Neurodegenerative Disorders; Neurons; Neurosciences; Patient Care; Patients; Pattern; Performance; Phase; Physicians; Physiological; Physiology; Population; Preparation; Process; Reproduction; Research; Research Proposals; Resolution; Rodent; Scientist; Sensory; Speech; Speech Sound; Staging; Stream; Symptoms; Synapses; Techniques; Training; Transgenic Mice; Translational Research; Voice; auditory feedback; behavior measurement; behavioral response; career; career development; cognitive function; design; effective therapy; experience; improved; innovation; millisecond; motor control; mouse model; network dysfunction; neural patterning; neurobehavioral; neurodegenerative dementia; neuroimaging; neurophysiology; novel; prevent; public health relevance; relating to nervous system; research study; response; spatiotemporal; speech processing; temporal measurement

 DESCRIPTION (provided by applicant): Alzheimer's disease (AD) is the most frequent neurodegenerative disorder and the most common cause of dementia in the elderly. Unfortunately, there are still no effective treatments to prevent, halt, or reverse AD. As new clinical trials focus on the initial stages of disease, early diagnosis is now more important than ever. Human neuroimaging has greatly improved our knowledge of this disease by demonstrating selectively vulnerable large-scale networks whose connectivity declines in AD. Transgenic mouse models of AD have identified specific brain regions whose aberrant excitatory activity causes neuronal network dysfunction. However, the exact relationship between neural network abnormalities in animal models with that in the human condition remains unknown. To better understand the altered neural dynamics underlying clinical symptoms in AD will require novel techniques and methodologies. Speech and language abnormalities are evident in AD patients from the very early stages of the disease. We propose to use a novel magnetoencephalographic imaging (MEGI) approach to determine the speech- motor-network deficits of AD-spectrum patients. MEGI provides direct recordings of brain activity with precise millisecond resolution. The goal of this study is to understand the spatial patterns and temporal dynamics of speech encoding and speech execution in AD patients. Using customized software we will measure brain activity patterns across high and low frequency bands. We expect to find unique signatures of speech-motor- network dysconnectivity in AD. Such knowledge will be related to underlying synaptic dysfunctions of vulnerable neuronal populations as early biomarkers of the disease and will have important implications for translational research. The candidate is a physician-scientist with a strong commitment to a career in neurodegenerative dementia research. The candidate has an MD and a PhD in cognition and neuroscience. The research proposal and career development plan build upon her training in rodent neurophysiology and auditory and speech processing to provide expertise in human brain connectivity and its relationship to cognitive deficits in AD. Dr. Keith Vossel, a physician-scientist who cares for patients with dementia and specializes in transgenic mouse models of neurodegenerative disease, is the candidate's sponsor. The mentoring and research experience described in this proposal will facilitate the candidate's goal of developing a strong independent research career.

 描述（由适用提供）：阿尔茨海默氏病（AD）是最常见的神经退行性疾病，也是最古老的痴呆症最常见的原因。不幸的是，仍然没有有效的治疗方法可以预防，停止或逆转广告。随着新的临床试验着重于疾病的初始阶段，现在早期诊断比以往任何时候都更为重要。人类神经影像学大大改善了我们对这种疾病的了解，通过证明其连通性在AD中有所下降的有选择性易受攻击的大规模网络。 AD的转基因小鼠模型已经确定了特定的大脑区域，其异常兴奋活动会导致神经元网络功能障碍。但是，动物模型中神经元网络异常与人类状况的确切关系仍然未知。为了更好地了解AD中临床症状的基础神经元动力学改变将需要新颖的技术和方法。言语和语言异常是疾病早期阶段的AD患者的证据。我们建议使用一种新型的磁脑影像学成像（MEGI）方法来确定Ad-Spectrum患者的语音运动网络定义。 MEGI通过精确的毫秒分辨率提供了大脑活动的直接记录。这项研究的目的是了解AD患者的语音编码和语音执行的空间模式和临时动态。使用自定义软件，我们将测量高频和低频带的大脑活动模式。我们希望在AD中找到语音运动 - 网络dysconnectectivity的独特签名。这些知识将与脆弱神经元人群的潜在突触功能障碍有关，作为该疾病的早期生物标志物，并将对翻译研究具有重要意义。候选人是一个身体科学家，对神经退行性痴呆研究的职业有坚定的承诺。候选人拥有MD和认知和神经科学博士学位。研究建议和职业发展计划基于她在啮齿动物神经生理学以及听觉和语音处理方面的培训，以提供人类脑连通性的专业知识及其与AD认知缺陷的关系。身体科学家基思·沃塞尔（Keith Vossel）是候选人的赞助商，他是一个身体科学家，专门研究痴呆症患者，专门研究神经退行性疾病的转基因小鼠模型。该提案中描述的心理和研究经验将有助于候选人发展强大的独立研究职业的目标。