THE DYNAMICS OF LONG RANGE CORRELATIONS IN CORTEX: SINGLE UNITS AND OXYGEN

皮层中长程相关性的动力学：单个单元和氧气

基本信息

批准号：
9457753
负责人：
Lawrence H Snyder
金额：
$ 22.88万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-28 至 2019-09-27
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9457753
关键词：
Acute Address Animals Architecture Biological Neural Networks Brain Brain Injuries Calcium Signaling Caliber Cells Chronic Clinical Complex Data Development Diagnosis Disease Dyslexia Electrodes Electromagnetics Electrophysiology (science)Environment Fiber Frequencies Functional Magnetic Resonance Imaging Gold Human Individual Interneurons Knowledge Learning Skill Location Magnetic Resonance Imaging Measurement Measures Mental Depression Methods Microelectrodes Monkeys Mus Neurons Neurosciences Noise Optical Methods Oxygen Pathologic Pattern Play Polarography Primates Prosopagnosia Resolution Rest Role Sampling Shapes Signal Transduction Site Spatial Distribution Stimulus Structure System Testing Tissues Width Work awake base blood oxygen level dependent carbon fiber cell type clinical Diagnosis density design dynamic system executive function fascinate improved innovation insight millisecond miniaturize nonhuman primate novel novel strategies relating to nervous system response tool

项目摘要

ABSTRACT Resting state networks are a fascinating yet poorly understood phenomenon. Sets of spatially separated regions show correlated slow fluctuations in fMRI BOLD signals, most obvious when subjects are at rest. These networks appear to have clinical imporantance: brain injuries perturb resting state networks, and multiple clinical disorders, including depression, dyslexia and prosopagnosia, are associated with specific resting state network abnormalities. Resting state data are used to infer functional connections between regions, but little is known about how neuronal activity gives rise to these networks. Furthermore, despite much speculation, little is known about how resting network state might influence task-related neuronal activity. Understanding the reciprocal relationships between resting state networks and neural activity has the potential to revolutionize our understanding of brain function. The reason that a gap in our knowledge exists is primarily due to the fact that it is difficult to characterize resting state networks without being within an active MRI scanner, and difficult to record spikes from neurons in such an environment (and even more difficult to record from multiple cells at once in such an environment). Alternative methods most involve serial recording of resting state networks and neuronal activity, recording of lower frequency electrical signals (LFP), or the use of optical methods in mouse which provide a close but not exact surrogate of neuronal activity (e.g., calcium signals) and access only to the uppermost layers of cortex. We propose to develop an innovative method to address this issue: high density parallel recording and oxygen polarography using carbon fiber microwires widely dispersed across the cortex of an awake behaving non-human primate. We have already demonstrated long-range correlations using oxygen polarography recorded on standard size micro-electrodes, paired with standard unit-recording electrodes. These correlations resemble resting state phenomenon, but in order to capture and relate the dynamics of neural activity to the dynamics of resting state networks, much denser spatial sampling is required.

抽象的静息状态网络是一种令人着迷但人们知之甚少的现象。空间上的集合分离的区域显示出 fMRI BOLD 信号相关的缓慢波动，当受试者处于休息状态。这些网络似乎具有临床重要性：脑损伤扰乱静息状态网络和多种临床疾病，包括抑郁症、阅读障碍和面部失认症与特定的静息状态网络异常有关。静息状态数据用于推断区域之间的功能联系，但人们对如何推断知之甚少神经元活动产生了这些网络。此外，尽管有很多猜测，但很少有了解静息网络状态如何影响与任务相关的神经元活动。了解静息状态网络和神经活动之间的相互关系有潜力彻底改变我们对大脑功能的理解。我们的知识之所以存在差距，主要是因为我们很难无需处于活动 MRI 扫描仪内即可表征静息状态网络，并且很难在这样的环境中记录神经元的尖峰（甚至更难以记录）在这样的环境中同时使用多个细胞）。替代方法大多数涉及串行记录静息状态网络和神经元活动，记录低频电信号（LFP），或在小鼠中使用光学方法，提供接近但不精确的替代神经元活动（例如钙信号）并且仅访问皮质的最上层。我们建议开发一种创新方法来解决这个问题：高密度并行使用广泛分布在整个区域的碳纤维微丝进行记录和氧极谱分析清醒行为的非人类灵长类动物的皮层。我们已经展示了远程使用在标准尺寸微电极上记录的氧极谱法进行相关性，并与标准单位记录电极。这些相关性类似于静息态现象，但是为了捕获神经活动的动态并将其与静息状态的动态联系起来网络，需要更密集的空间采样。