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Exploring the Neural Dynamics of Cognition through Human Electrocorticography

Exploring the Neural Dynamics of Cognition through Human Electrocorticography
通过人体皮层电图探索认知的神经动力学
批准号:
0642848
负责人:
Rajesh Rao
金额:
$61.37万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-04-15 至 2010-03-31

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中文摘要
翻译
为了解开控制认知的神经机制,人们必须了解不同的大脑区域如何在几十到几百毫秒的时间尺度上相互作用。毫秒范围内的时间分辨率很难通过成像技术(如fMRI)实现。另一方面,用于灵长类动物的电生理技术提供了高时间分辨率,但一次只能记录单个或最多几十个神经元。克服许多这些问题的替代记录技术是皮层电图(ECoG),其中在手术前植入用于评估大脑的电极阵列用于记录来自人的表面的电波动。ECoG允许同时测量来自几个不同大脑区域的电信号,同时提供毫秒范围内的时间分辨率。因此,它特别适合于探索认知的神经动力学。在国家科学基金会的支持下,华盛顿大学的拉杰什·拉奥和杰夫·奥杰曼博士将使用代表认知中三个不同抽象层次的三个任务来检查皮质动力学:运动任务、工作记忆任务和语言任务。 它将使用传统的频谱技术以及更复杂的统计技术,如用于源分离的独立分量分析(伊卡)和用于信号估计的贝叶斯技术来分析数据。 由此,他们将构建神经元网络的生物物理模型,并模拟皮质-皮质和皮质-丘脑反馈回路,以了解ECoG的神经起源。 如果成功,这项研究将使人们对大脑功能有一个新的认识,从长远来看,这将有助于治疗涉及运动控制、记忆或语言处理的认知缺陷。
英文摘要
To unravel the neural mechanisms governing cognition, one must understand how different brain areas interact with one another on time scales that range from tens to several hundreds of milliseconds. Temporal resolution in the range of milliseconds is hard to achieve through imaging techniques such as fMRI. On the other hand, electrophysiological techniques used in primates provide high temporal resolution but only record from a single or at most a few tens of neurons at a time. An alternative recording technique that overcomes many of these problems is electrocorticography (ECoG) where an array of electrodes, implanted for assessment of the brain before surgery, is used to record electrical fluctuations from the surface of the human. ECoG allows electrical signals from several different brain areas to be measured simultaneously while at the same time providing temporal resolution in the millisecond range. It is thus uniquely suited for probing the neural dynamics of cognition. With NSF support, Drs. Rajesh Rao and Jeff Ojemann of the University of Washington will examine cortical dynamics using three tasks that represent three different levels of abstraction in cognition: a motor movement task, a working memory task, and a language task. It will analyze the data using conventional spectral techniques as well as more sophisticated statistical techniques such as Independent Component Analysis (ICA) for source separation and Bayesian techniques for signal estimation. From this, they will construct biophysical models of networks of neurons and simulating cortico-cortical and cortico-thalamic feedback loops to understand the neural genesis of ECoG. If successful, this research will allow a new understanding of brain function, leading in the long term to possible remedies for cognitive deficits involving motor control, memory, or language processing.
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RI: Small: Probabilistic Goal-Based Imitation Learning
  • 批准号:
    1318733
  • 项目类别:
    Standard Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2013
  • 负责人:
    Rajesh Rao
  • 依托单位:
NSF Engineering Research Center for Sensorimotor Neural Engineering
  • 批准号:
    1028725
  • 项目类别:
    Cooperative Agreement
  • 资助金额:
    $1817.5万
  • 财政年份:
    2011
  • 负责人:
    Rajesh Rao
  • 依托单位:
Electrocorticographic Brain-Machine Interfaces for Communication and Prosthetic Control
  • 批准号:
    0930908
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2009
  • 负责人:
    Rajesh Rao
  • 依托单位:
BIC: Probabilistic Neural Computation: Models and Applications in Robotics and Brain-Machine Interfaces
  • 批准号:
    0622252
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $0.0万
  • 财政年份:
    2006
  • 负责人:
    Rajesh Rao
  • 依托单位:
国内基金
海外基金
Neural Process模型的多样化高保真技术研究