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.
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.
|Bentley, William J; Li, Jingfeng M; Snyder, Abraham Z et al. (2016) Oxygen Level and LFP in Task-Positive and Task-Negative Areas: Bridging BOLD fMRI and Electrophysiology. Cereb Cortex 26:346-57|
|Li, Jingfeng M; Bentley, William J; Snyder, Abraham Z et al. (2015) Functional connectivity arises from a slow rhythmic mechanism. Proc Natl Acad Sci U S A 112:E2527-35|