A MICRO-ELECTRODE STUDY OF OXYGEN-BASED FUNCTIONAL CONNECTIVITY

描述（由申请人提供）：静息状态网络是人类认知神经科学中一个令人着迷但知之甚少的新现象。即使在受试者休息时，空间分离的区域组在fMRI BOLD信号中也显示出相关的缓慢波动。这些网络似乎对正常的大脑功能很重要：行为表现的各个方面可以通过持续的缓慢相关的BOLD波动水平来预测；脑损伤扰乱静息状态网络；多种临床疾病，包括抑郁症、阅读障碍和面孔失认症，都与特定的静息状态网络异常有关。目前，静息状态数据被用来推断区域之间的功能联系，但对因果关系、空间和时间尺度或相关的潜在神经基质知之甚少。对慢波动和静息状态网络的深入了解对于理解正常和无序认知具有巨大的潜力。我们试图通过在高空间和时间频率上表征相关波动并识别与之相关的电生理信号来更好地理解相关波动的起源和意义。这项工作的意义在于，我们将能够更好地利用已经收集到的功能磁共振成像信息，改进诊断，也许揭示几种神经系统疾病的病因，可能发现以前未被怀疑的大脑运作模式，并普遍获得对认知处理的新见解。创新与方法：为了获得这些数据，我们建议以一种新的方式使用经典的氧极谱技术。在静息状态功能磁共振成像扫描的指导下，我们将在猕猴大脑中插入多个铂微电极，以验证和表征氧浓度的相关波动。然后，我们将同时记录来自相同或相邻电极的电生理信号，并询问电生理频谱的哪一部分（慢皮层电位、局部场电位、多单元活动）与相关（静息状态网络）氧波动相关。这是解决这一问题的一种新方法，我们在猴子电生理学（L. Snyder, a . Snyder）、人类功能磁共振成像（M. Raichle, a . Snyder）、人类静息状态网络分析（M. Raichle, a . Snyder）和猴子功能磁共振成像（L. Snyder， M. Raichle, a . Snyder）方面有必要的专业知识。我们已经在华盛顿大学合作建立了解剖和功能猴子的fMRI，我们一起发表了显示猴子的静息状态网络与人类非常相似的数据。

DESCRIPTION (provided by applicant): Resting state networks are a fascinating yet poorly understood new phenomenon in human cognitive neuroscience. Sets of spatially separated regions show correlated slow fluctuations in fMRI BOLD signals even when the subject is at rest. These networks appear to be important in normal brain function: aspects of behavioral performance can be predicted by the ongoing level of slow correlated BOLD fluctuations; brain injuries perturb resting state networks; and multiple clinical disorders, including depression, dyslexia and prosopagnosia, are associated with specific resting state network abnormalities. Currently, resting state data are used to infer functional connections between regions, but little is known about causality, spatial and temporal scale, or the underlying neural substrate of the correlations. A deeper understanding of slow fluctuations and resting state networks has enormous potential for understanding normal and disordered cognition. We seek to better understand the origin and significance of correlated fluctuations by characterizing them at high spatial and temporal frequencies and identifying the electrophysiological signals that are associated with them. The significance of this work is that we will be able to make better use of the fMRI information already being collected, improve diagnosis and perhaps reveal the etiology of several neurological disorders, possibly discover previously unsuspected modes of brain operation, and generally obtain new insight into cognitive processing. Innovation & approach: To obtain these data we propose to use a classical technique, oxygen polarography, in a new way. Guided by resting state fMRI scans, we will insert multiple platinum microelectrodes into a macaque brain in order to verify and characterize correlated fluctuations in oxygen concentration. We will then record simultaneous electrophysiological signals from the same or adjacent electrodes and ask what portion of the electrophysiological spectrum (slow cortical potentials, local field potentials, multi-unit activity) is associated with correlated (resting state network) oxygen fluctuations. This is a new approach to this issue, and we have the required expertise in monkey electrophysiology (L. Snyder, A. Snyder), human fMRI (M. Raichle, A. Snyder), human resting state network analysis (M. Raichle, A. Snyder) and monkey fMRI (L. Snyder, M. Raichle, A. Snyder) to be successful. We have already worked together to establish anatomical and functional monkey fMRI at Washington University, and together we have published data showing resting state networks in the monkey that closely resemble those in humans. PUBLIC HEALTH RELEVANCE: A new methodology, functional connectivity MRI (fcMRI), has recently been applied to diagnose and understand the etiology of a range of diseases and disorders. FcMRI looks at long-distance correlations in brain oxygen to draw inferences about the fundamental structure of the brain and pathological disturbances in that structure. The technique holds great clinical promise, but we currently have very little understanding of why these long-distance correlations exist or what they mean. This grant will provide new information about the origin and interpretation of these correlations, which in turn will greatly increase the amount of clinical information we can extract from the method. In particular, it will improve the diagnosis and understanding of the etiology of the conditions in which it is being currently applied, which include stroke, prosopagnosia and dyslexia.