The overall aim of this proposal is to determine whether inter-regional correlations in resting state fluctuations of magnetic resonance (MRI) signals from the brain reliably measure functional connectivity between regions. The identification of patterns of highly correlated low frequency MRI signals in the resting state potentially provides a powerful approach to delineate and describe neural circuits. Moreover, observations of altered resting state connectivity in several disorders suggest these correlations reflect an important level of brain organization. However, although resting state correlations are already being widely used to assess brain functional architecture, their precise interpretation remains unclear, and whether they are direct indicators of functional connectivity is completely unsubstantiated. To investigate connectivity we will use very high- resolution MRI at high field (9.4T) to delineate cortical networks in anesthetized non-human primates. The sub- regions of SI cortex in monkeys are an excellent experimental model because the functional and anatomical structures of this region have previously been well investigated with invasive electrophysiological and histological methods. At 9.4T, fMRI acquisitions using vibrotactile stimuli identify functionally distinct cortical areas (3a, 3b and 1) within SI cortex, and each displays distinct fine-scale intra- and inter-regional, as well as longer range cortico-thalamic connectivity. We will (a) use stimulus-driven activation maps to identify candidate areas, and then measure resting state spatial connectivity patterns at sub-millimeter resolution in monkey brain;(b) measure the intrinsic point spread function of resting state BOLD, CBF (cerebral blood flow) and CBV (cerebral blood volume) correlations;and (c) determine how inter-regional correlations vary with functional role, spatial resolution and between BOLD, CBF and CBV signals. We will use simple multivariate models to reduce the data to a format by which they can be directly compared to electrophysiological measurements. We will then validate the measurements of connectivity from resting state MRI signals by direct comparisons with quantitative electrophysiology and histology in the same animals. We will (a) determine quantitatively the degree of spatial overlap of electrophysiologically defined neuronal response maps and resting state fMRI correlation maps: (b) determine what features of the spontaneous electrical recordings (multiple-unit spiking and local field potentials) from identified regions shw patterns of correlation similar to BOLD: (c) determine whether areas which appear to be functionally connected by BOLD and electrophysiology also exhibit strong inter-areal anatomical connections by injecting anatomical tracers into candidate regions identified by electrical mapping and fMRI and performing histological assessments post mortem. We believe that the proposed studies have considerable importance for better understanding the neural basis of resting state functional connectivity measures, and will have direct implications and impact on the applications of fMRI in both basic and clinical neuroscience.

Public Health Relevance

The analysis of series of magnetic resonance images of the brain acquired over a period of minutes while the subject is in a resting state, and not engaged in any specific mental activity or task, reveals strong inter-regional correlations of the baseline MRI signal that appear to reflect identifiable neural circuits. There is considerable potential in exploiting this functional connectivity, but thus far there have been no studies to validate that these signal variations reveal actual anatomic connectivity or correlated variations in neural electrical activity. The studies proposed would establish these links by looking at very high resolution in the brains of non-human primates.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BMIT-J (01))
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Babcock, Debra J
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Vanderbilt University Medical Center
Schools of Medicine
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Wang, Feng; Li, Ke; Mishra, Arabinda et al. (2016) Longitudinal assessment of spinal cord injuries in nonhuman primates with quantitative magnetization transfer. Magn Reson Med 75:1685-96
Wilson 3rd, George H; Yang, Pai-Feng; Gore, John C et al. (2016) Correlated inter-regional variations in low frequency local field potentials and resting state BOLD signals within S1 cortex of monkeys. Hum Brain Mapp 37:2755-66
Shi, Zhaoyue; Rogers, Baxter P; Chen, Li Min et al. (2016) Realistic models of apparent dynamic changes in resting-state connectivity in somatosensory cortex. Hum Brain Mapp 37:3897-3910
Wu, Tung-Lin; Wang, Feng; Anderson, Adam W et al. (2016) Effects of anesthesia on resting state BOLD signals in white matter of non-human primates. Magn Reson Imaging 34:1235-1241
Ding, Zhaohua; Xu, Ran; Bailey, Stephen K et al. (2016) Visualizing functional pathways in the human brain using correlation tensors and magnetic resonance imaging. Magn Reson Imaging 34:8-17
Yang, Pai-Feng; Wang, Feng; Chen, Li Min (2015) Differential fMRI Activation Patterns to Noxious Heat and Tactile Stimuli in the Primate Spinal Cord. J Neurosci 35:10493-502
Chen, Li Min; Mishra, Arabinda; Yang, Pai-Feng et al. (2015) Injury alters intrinsic functional connectivity within the primate spinal cord. Proc Natl Acad Sci U S A 112:5991-6
Wang, Feng; Qi, Hui-Xin; Zu, Zhongliang et al. (2015) Multiparametric MRI reveals dynamic changes in molecular signatures of injured spinal cord in monkeys. Magn Reson Med 74:1125-37
Mishra, Arabinda; Rogers, Baxter P; Chen, Li Min et al. (2014) Functional connectivity-based parcellation of amygdala using self-organized mapping: a data driven approach. Hum Brain Mapp 35:1247-60
Katwal, Santosh B; Gore, John C; Marois, Rene et al. (2013) Unsupervised spatiotemporal analysis of fMRI data using graph-based visualizations of self-organizing maps. IEEE Trans Biomed Eng 60:2472-83

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