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      Brain Morphological Signatures for Chronic Pain

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          Abstract

          Chronic pain can be understood not only as an altered functional state, but also as a consequence of neuronal plasticity. Here we use in vivo structural MRI to compare global, local, and architectural changes in gray matter properties in patients suffering from chronic back pain (CBP), complex regional pain syndrome (CRPS) and knee osteoarthritis (OA), relative to healthy controls. We find that different chronic pain types exhibit unique anatomical ‘brain signatures’. Only the CBP group showed altered whole-brain gray matter volume, while regional gray matter density was distinct for each group. Voxel-wise comparison of gray matter density showed that the impact on the extent of chronicity of pain was localized to a common set of regions across all conditions. When gray matter density was examined for large regions approximating Brodmann areas, it exhibited unique large-scale distributed networks for each group. We derived a barcode, summarized by a single index of within-subject co-variation of gray matter density, which enabled classification of individual brains to their conditions with high accuracy. This index also enabled calculating time constants and asymptotic amplitudes for an exponential increase in brain re-organization with pain chronicity, and showed that brain reorganization with pain chronicity was 6 times slower and twice as large in CBP in comparison to CRPS. The results show an exuberance of brain anatomical reorganization peculiar to each condition and as such reflecting the unique maladaptive physiology of different types of chronic pain.

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          Most cited references44

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          Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm.

          The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation--no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.
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            Chronic back pain is associated with decreased prefrontal and thalamic gray matter density.

            The role of the brain in chronic pain conditions remains speculative. We compared brain morphology of 26 chronic back pain (CBP) patients to matched control subjects, using magnetic resonance imaging brain scan data and automated analysis techniques. CBP patients were divided into neuropathic, exhibiting pain because of sciatic nerve damage, and non-neuropathic groups. Pain-related characteristics were correlated to morphometric measures. Neocortical gray matter volume was compared after skull normalization. Patients with CBP showed 5-11% less neocortical gray matter volume than control subjects. The magnitude of this decrease is equivalent to the gray matter volume lost in 10-20 years of normal aging. The decreased volume was related to pain duration, indicating a 1.3 cm3 loss of gray matter for every year of chronic pain. Regional gray matter density in 17 CBP patients was compared with matched controls using voxel-based morphometry and nonparametric statistics. Gray matter density was reduced in bilateral dorsolateral prefrontal cortex and right thalamus and was strongly related to pain characteristics in a pattern distinct for neuropathic and non-neuropathic CBP. Our results imply that CBP is accompanied by brain atrophy and suggest that the pathophysiology of chronic pain includes thalamocortical processes.
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              Towards a theory of chronic pain.

              In this review, we integrate recent human and animal studies from the viewpoint of chronic pain. First, we briefly review the impact of chronic pain on society and address current pitfalls of its definition and clinical management. Second, we examine pain mechanisms via nociceptive information transmission cephalad and its impact and interaction with the cortex. Third, we present recent discoveries on the active role of the cortex in chronic pain, with findings indicating that the human cortex continuously reorganizes as it lives in chronic pain. We also introduce data emphasizing that distinct chronic pain conditions impact on the cortex in unique patterns. Fourth, animal studies regarding nociceptive transmission, recent evidence for supraspinal reorganization during pain, the necessity of descending modulation for maintenance of neuropathic behavior, and the impact of cortical manipulations on neuropathic pain is also reviewed. We further expound on the notion that chronic pain can be reformulated within the context of learning and memory, and demonstrate the relevance of the idea in the design of novel pharmacotherapies. Lastly, we integrate the human and animal data into a unified working model outlining the mechanism by which acute pain transitions into a chronic state. It incorporates knowledge of underlying brain structures and their reorganization, and also includes specific variations as a function of pain persistence and injury type, thereby providing mechanistic descriptions of several unique chronic pain conditions within a single model.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                13 October 2011
                : 6
                : 10
                : e26010
                Affiliations
                [1 ]Department of Physiology, Northwestern University, Chicago, Illinois, United States of America
                [2 ]Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois, United States of America
                [3 ]Department of Neuroscience, University of Toledo, Toledo, Ohio, United States of America
                [4 ]Department of Anesthesia, Feinberg School of Medicine, Chicago, Illinois, United States of America
                [5 ]Department of Surgery, Feinberg School of Medicine, Chicago, Illinois, United States of America
                University of Cordoba, Spain
                Author notes

                Conceived and designed the experiments: AVA MNB. Performed the experiments: MNB. Analyzed the data: AVA MNB. Contributed reagents/materials/analysis tools: AVA MNB TJS WRB. Wrote the paper: AVA MNB.

                Article
                PONE-D-11-15115
                10.1371/journal.pone.0026010
                3192794
                22022493
                d67a2ab8-3f22-428c-9955-1d610fe6b349
                Baliki et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 27 July 2011
                : 15 September 2011
                Page count
                Pages: 13
                Categories
                Research Article
                Biology
                Anatomy and Physiology
                Neurological System
                Neuroanatomy
                Neuroscience
                Cognitive Neuroscience
                Pain
                Neuroanatomy
                Neuroimaging
                Medicine
                Anatomy and Physiology
                Neurological System
                Neuroanatomy
                Anesthesiology
                Pain Management
                Neurology
                Neuroimaging
                Pain Management
                Radiology
                Diagnostic Radiology
                Magnetic Resonance Imaging

                Uncategorized
                Uncategorized

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