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      Sensorimotor and Pain Modulation Brain Abnormalities in Trigeminal Neuralgia: A Paroxysmal, Sensory-Triggered Neuropathic Pain

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          Abstract

          Objective

          Idiopathic trigeminal neuralgia (TN) is characterized by paroxysms of severe facial pain but without the major sensory loss that commonly accompanies neuropathic pain. Since neurovascular compression of the trigeminal nerve root entry zone does not fully explain the pathogenesis of TN, we determined whether there were brain gray matter abnormalities in a cohort of idiopathic TN patients. We used structural MRI to test the hypothesis that TN is associated with altered gray matter (GM) in brain areas involved in the sensory and affective aspects of pain, pain modulation, and motor function. We further determined the contribution of long-term TN on GM plasticity.

          Methods

          Cortical thickness and subcortical GM volume were measured from high-resolution 3T T1-weighted MRI scans in 24 patients with right-sided TN and 24 healthy control participants.

          Results

          TN patients had increased GM volume in the sensory thalamus, amygdala, periaqueductal gray, and basal ganglia (putamen, caudate, nucleus accumbens) compared to healthy controls. The patients also had greater cortical thickness in the contralateral primary somatosensory cortex and frontal pole compared to controls. In contrast, patients had thinner cortex in the pregenual anterior cingulate cortex, the insula and the orbitofrontal cortex. No relationship was observed between GM abnormalities and TN pain duration.

          Conclusions

          TN is associated with GM abnormalities in areas involved in pain perception, pain modulation and motor function. These findings may reflect increased nociceptive input to the brain, an impaired descending modulation system that does not adequately inhibit pain, and increased motor output to control facial movements to limit pain attacks.

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          Most cited references 49

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          Fast robust automated brain extraction.

           Teri S Krebs (2002)
          An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
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            Improved optimization for the robust and accurate linear registration and motion correction of brain images.

            Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.
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              Voxel-based morphometry--the methods.

              At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data. Copyright 2000 Academic Press.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                18 June 2013
                : 8
                : 6
                Affiliations
                [1 ]Division of Brain, Imaging and Behaviour- Systems Neuroscience, Toronto Western Research Institute, University Health Network, Toronto, Canada
                [2 ]Institute of Medical Science, University of Toronto, Toronto, Canada
                [3 ]Division of Neurosurgery, Toronto Western Hospital & University of Toronto, Toronto, Canada
                The Hebrew University Medical School, Israel
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KDD MH DDD. Performed the experiments: DDD MM DQC. Analyzed the data: DDD MM DQC. Contributed reagents/materials/analysis tools: DDD MM DCQ. Wrote the paper: DDD MH KDD.

                Article
                PONE-D-12-39677
                10.1371/journal.pone.0066340
                3688879
                23823184

                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.

                Counts
                Pages: 8
                Funding
                This work was supported by the PSI Foundation, the Canadian Institutes of Health Research Strategic Training Fellowship, TGF-53877, and the University of Toronto/Purdue Pharma OGSST award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine
                Anatomy and Physiology
                Neurological System
                Central Nervous System
                Nervous System Components
                Nervous System Physiology
                Neuroanatomy
                Sensory Physiology
                Neurology
                Facial Nerve Disorders
                Trigeminal Nerve Disorders
                Neuroimaging
                Pain Management

                Uncategorized

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