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: 9231998
• 负责人: Kamalini Gayathree Ranasinghe
• 金额: $ 6.53万
• 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/9231998
• 关键词: Affect; Age; Alzheimer' s Disease; Alzheimer' s disease model; Animal Model; Area; Auditory area; Awareness; Behavioral; Bilateral; Biological Markers; Biological Neural Networks; Brain; Brain Diseases; Brain region; Clinical; Clinical Trials; Cognition; Cognitive deficits; Communication; Computer software; Custom; Dementia; Deterioration; Development Plans; Disease; Doctor of Philosophy; Dorsal; Early Diagnosis; Elderly; Feedback; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Hearing; Human; Impaired cognition; Impairment; Individual; Knowledge; Language; Language Disorders; Magnetoencephalography; Measures; Mentors; Methodology; Methods; Motor; Neurobehavioral Manifestations; Neurodegenerative Disorders; Neurons; Neurosciences; Pathologic; Patient Care; Patients; Pattern; Performance; Phase; Physicians; Physiological; Physiology; Population; Preparation; Reproduction; Research; Research Proposals; Resolution; Rodent; Scientist; Sensory; Speech; Speech Sound; Stream; Symptoms; Synapses; Techniques; Training; Transgenic Mice; Translational Research; Voice; auditory feedback; auditory processing; behavior measurement; behavioral response; career; career development; cognitive function; design; effective therapy; experience; experimental study; imaging approach; imaging study; 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; 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，也是老年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，停止或逆转AD。随着新的临床试验集中在疾病的初始阶段，早期诊断现在比以往任何时候都更重要。 人类神经影像学通过展示AD中连接性下降的选择性脆弱的大规模网络，极大地提高了我们对这种疾病的认识。AD转基因小鼠模型已经确定了特定的大脑区域，其异常兴奋活动导致神经元网络功能障碍。然而，动物模型中的神经网络异常与人类状况之间的确切关系仍然未知。为了更好地了解AD临床症状背后的神经动力学改变，需要新的技术和方法。 言语和语言异常在AD患者中从疾病的非常早期阶段就很明显。我们建议使用一种新的脑磁图成像（MEGI）的方法来确定AD频谱患者的言语运动网络缺陷。MEGI以精确的毫秒分辨率直接记录大脑活动。本研究的目的是了解AD患者言语编码和言语执行的空间模式和时间动态。使用定制的软件，我们将测量高频段和低频段的大脑活动模式。我们希望在AD中找到语音-运动-网络连接障碍的独特特征。这些知识将与脆弱神经元群体的潜在突触功能障碍相关，作为疾病的早期生物标志物，并将对转化研究产生重要影响。 候选人是一名医生，科学家，对神经退行性痴呆研究的职业生涯有着坚定的承诺。候选人拥有认知和神经科学的医学博士和博士学位。该研究提案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，以提供人类大脑连接及其与AD认知缺陷关系的专业知识。基思沃塞尔博士是一位医生兼科学家，他照顾痴呆症患者，专门研究神经退行性疾病的转基因小鼠模型。本提案中描述的指导和研究经验将有助于候选人发展强大的独立研究事业的目标。