Oxygen levels within the human brain fluctuate without any apparent external driver. Unexpectedly, these intrinsic fluctuations are correlated among distant regions, forming "resting state networks". These networks appear to be relevant to brain function. Resting state data can provide evidence for functional connections between brain regions. Aspects of behavioral performance can be predicted by the ongoing level of slow correlated BOLD fluctuations. Finally, multiple neurological and psychiatric disorders including autism and schizophrenia are associated with abnormalities in resting state networks. Despite their potential importance for understanding normal and disordered cognition, resting state networks remain a poorly understood phenomenon in human cognitive neuroscience. We seek to better understand the origin and significance of correlated oxygen fluctuations by characterizing them at high spatial and temporal resolution and identifying the electrophysiological signals associated with them both at rest and during task performance. We will use oxygen polarography in a novel way. Guided initially by resting state fMRI scans, we will insert multiple platinum microelectrodes into a macaque brain to verify and characterize correlated fluctuations in oxygen concentration. We will record simultaneous electrophysiological signals from these electrodes and ask what portion of the electrophysiological spectrum (slow cortical potentials, local field potentials, multi-unit activity) is associated with task-driven and/or with resting-state correlated oxygen fluctuations. To accomplish this, we will exploit the advantages of polarography over fMRI, including co- localized and simultaneous oxygen and electrical signals, higher spatial and temporal resolution, resistance to movement artifacts, and ease of use in awake behaving animals. Our overall aim is to determine the transfer function mapping electrophysiology signals onto oxygen fluctuations, and whether this transfer function is network-specific, depends on the cortical layer being recorded from, or reflects the ongoing behavioral state of the animal (e.g., task-engaged, sleeping and under anesthesia). The clinical significance of this work is that it will lead to improved use of fMRI information for the diagnosis, prognosis and etiology of brain disorders. The scientific significance, at a high level, is that it will inform our understanding of large-scale brain architecture and cognitive processing.
Functional connectivity MRI (fcMRI) is the premier method for studying human cognitive architecture. An explosion of studies address brain function, diagnosis and disease etiology with this technique, in which the pattern and statistics of long-distance correlations in brain oxygen are analyzed in order to draw inferences about fundamental brain structure and pathology. Remarkably, however, we have only a rudimentary understanding of the neural activity underlying these correlations. Studies are limited by constraints on human recording and by interference between the MRI machine and the electrophysiology. This grant will study non-human primates instead of humans using a new method, oxygen polarography, to replace the MRI machine. By recording simultaneous oxygen and electrophysiology signals we will obtain new information about their linkage, and we will enable future interventional experiments to test hypotheses regarding their origin and effect. This will greatly enhance the basic scientific and clinical value of the method. In particular, it will improve the diagnosis, prognosis and etiological understanding of multiple brain diseases ranging from acute neurological events like strokes to developmental and psychiatric conditions like schizophrenia and autism.
|Bentley, William J; Li, Jingfeng M; Snyder, Abraham Z et al. (2014) Oxygen Level and LFP in Task-Positive and Task-Negative Areas: Bridging BOLD fMRI and Electrophysiology. Cereb Cortex :|
|Patel, Gaurav H; Kaplan, David M; Snyder, Lawrence H (2014) Topographic organization in the brain: searching for general principles. Trends Cogn Sci 18:351-63|