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      Brain Rhythms of Pain

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          Abstract

          Pain is an integrative phenomenon that results from dynamic interactions between sensory and contextual (i.e., cognitive, emotional, and motivational) processes. In the brain the experience of pain is associated with neuronal oscillations and synchrony at different frequencies. However, an overarching framework for the significance of oscillations for pain remains lacking. Recent concepts relate oscillations at different frequencies to the routing of information flow in the brain and the signaling of predictions and prediction errors. The application of these concepts to pain promises insights into how flexible routing of information flow coordinates diverse processes that merge into the experience of pain. Such insights might have implications for the understanding and treatment of chronic pain.

          Trends

          Pain is a vital phenomenon that depends on the dynamic integration of sensory and contextual processes. In chronic pain the adaptive integration of sensory and contextual processes is severely disturbed.

          Neuronal oscillations and synchrony at different frequencies provide evidence on information flow across brain areas. The flexible relationship between oscillations at different frequencies and pain indicates flexible routing of information flow in the cerebral processing of pain.

          The systematic assessment of oscillations and synchrony in the processing of pain provides insights into how sensory and contextual processes are flexibly integrated into a coherent percept and into abnormalities of these processes in chronic pain. Predictive coding frameworks might help us understand these integration processes.

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          Most cited references70

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          Electrophysiological signatures of resting state networks in the human brain.

          Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.
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            Resting-state functional connectivity in neuropsychiatric disorders.

            This review considers recent advances in the application of resting-state functional magnetic resonance imaging to the study of neuropsychiatric disorders. Resting-state functional magnetic resonance imaging is a relatively novel technique that has several potential advantages over task-activation functional magnetic resonance imaging in terms of its clinical applicability. A number of research groups have begun to investigate the use of resting-state functional magnetic resonance imaging in a variety of neuropsychiatric disorders including Alzheimer's disease, depression, and schizophrenia. Although preliminary results have been fairly consistent in some disorders (for example, Alzheimer's disease) they have been less reproducible in others (schizophrenia). Resting-state connectivity has been shown to correlate with behavioral performance and emotional measures. It's potential as a biomarker of disease and an early objective marker of treatment response is genuine but still to be realized. Resting-state functional magnetic resonance imaging has made some strides in the clinical realm but significant advances are required before it can be used in a meaningful way at the single-patient level.
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              Investigating the electrophysiological basis of resting state networks using magnetoencephalography.

              In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenation-level-dependent (BOLD) signals from different brain areas. However, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.
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                Author and article information

                Contributors
                Journal
                Trends Cogn Sci
                Trends Cogn. Sci. (Regul. Ed.)
                Trends in Cognitive Sciences
                Elsevier Science
                1364-6613
                1879-307X
                1 February 2017
                February 2017
                : 21
                : 2
                : 100-110
                Affiliations
                [1 ]Department of Neurology and TUMNeuroimaging Center, Technische Universität München, Munich, Germany
                [2 ]Departments of Neuroradiology and Psychiatry and TUMNeuroimaging Center, Technische Universität München, Munich, Germany
                [3 ]Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
                Author notes
                [* ]Correspondence: markus.ploner@ 123456tum.de
                Article
                S1364-6613(16)30200-5
                10.1016/j.tics.2016.12.001
                5374269
                28025007
                7d20bd06-563d-4eb5-aa81-b920355d7cbe
                © 2016 The Authors. Published by Elsevier Ltd.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Categories
                Review

                Neurosciences
                pain,brain,oscillations,information flow,predictive coding
                Neurosciences
                pain, brain, oscillations, information flow, predictive coding

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