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(申请人提供)：阿尔茨海默病(AD)是最常见的神经退行性疾病，也是导致老年人痴呆症的最常见原因。不幸的是，仍然没有有效的治疗方法来预防、阻止或逆转AD。随着新的临床试验将重点放在疾病的初始阶段，早期诊断现在比以往任何时候都更加重要。人类神经成像通过显示有选择地脆弱的大规模网络，极大地提高了我们对这种疾病的认识，这些网络的连接性在AD中下降。转基因阿尔茨海默病小鼠模型已经确定了其异常兴奋活动导致神经元网络功能障碍的特定脑区。然而，动物模型中的神经网络异常与人类条件下的神经网络异常之间的确切关系尚不清楚。为了更好地了解阿尔茨海默病临床症状背后的神经动力学变化，需要新的技术和方法。从疾病的早期阶段起，阿尔茨海默病患者的言语和语言就明显异常。我们建议使用一种新的脑磁图成像(MEGI)方法来确定AD谱系患者的语言-运动-网络缺陷。MEGI提供精确毫秒分辨率的大脑活动的直接记录。本研究的目的是了解AD患者语音编码和语音执行的空间模式和时间动态。使用定制的软件，我们将测量高、低频段的大脑活动模式。我们期望在AD患者中发现独特的语音-运动-网络连接障碍的特征。这些知识将与作为疾病早期生物标志物的脆弱神经元群体潜在的突触功能障碍有关，并将对翻译研究具有重要意义。候选人是一名内科科学家，对神经退行性痴呆症的研究有着坚定的承诺。候选人拥有认知科学和神经科学的医学博士学位和博士学位。研究建议和职业发展计划建立在她在啮齿动物神经生理学和听觉和语音处理方面的培训基础上，以提供关于人脑连接及其与AD认知缺陷的关系的专业知识。基思·沃塞尔博士是候选人的赞助人，他是一名内科科学家，照顾痴呆症患者，专门研究神经退行性疾病的转基因小鼠模型。这份提案中描述的指导和研究经验将有助于候选人实现发展强大的独立研究事业的目标。