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      Decoding the perception of pain from fMRI using multivariate pattern analysis

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          Abstract

          Pain is known to comprise sensory, cognitive, and affective aspects. Despite numerous previous fMRI studies, however, it remains open which spatial distribution of activity is sufficient to encode whether a stimulus is perceived as painful or not. In this study, we analyzed fMRI data from a perceptual decision-making task in which participants were exposed to near-threshold laser pulses. Using multivariate analyses on different spatial scales, we investigated the predictive capacity of fMRI data for decoding whether a stimulus had been perceived as painful. Our analysis yielded a rank order of brain regions: during pain anticipation, activity in the periaqueductal gray (PAG) and orbitofrontal cortex (OFC) afforded the most accurate trial-by-trial discrimination between painful and non-painful experiences; whereas during the actual stimulation, primary and secondary somatosensory cortex, anterior insula, dorsolateral and ventrolateral prefrontal cortex, and OFC were most discriminative. The most accurate prediction of pain perception from the stimulation period, however, was enabled by the combined activity in pain regions commonly referred to as the ‘pain matrix’. Our results demonstrate that the neural representation of (near-threshold) pain is spatially distributed and can be best described at an intermediate spatial scale. In addition to its utility in establishing structure-function mappings, our approach affords trial-by-trial predictions and thus represents a step towards the goal of establishing an objective neuronal marker of pain perception.

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          Highlights

          ► Subjects received a series of near-threshold pain stimuli while undergoing fMRI. ► We studied multivariate patterns of brain activity underlying pain perception. ► PAG, VLPFC, DLPFC, anterior insula, and OFC were highly predictive of pain. ► Yet the highest accuracies were afforded by combinations of these regions. ► This suggests that pain is encoded in a distributed network.

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          Most cited references22

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          Empathy for pain involves the affective but not sensory components of pain.

          Our ability to have an experience of another's pain is characteristic of empathy. Using functional imaging, we assessed brain activity while volunteers experienced a painful stimulus and compared it to that elicited when they observed a signal indicating that their loved one--present in the same room--was receiving a similar pain stimulus. Bilateral anterior insula (AI), rostral anterior cingulate cortex (ACC), brainstem, and cerebellum were activated when subjects received pain and also by a signal that a loved one experienced pain. AI and ACC activation correlated with individual empathy scores. Activity in the posterior insula/secondary somatosensory cortex, the sensorimotor cortex (SI/MI), and the caudal ACC was specific to receiving pain. Thus, a neural response in AI and rostral ACC, activated in common for "self" and "other" conditions, suggests that the neural substrate for empathic experience does not involve the entire "pain matrix." We conclude that only that part of the pain network associated with its affective qualities, but not its sensory qualities, mediates empathy.
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            Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data.

            In the present study, we applied the Support Vector Machine (SVM) algorithm to perform multivariate classification of brain states from whole functional magnetic resonance imaging (fMRI) volumes without prior selection of spatial features. In addition, we did a comparative analysis between the SVM and the Fisher Linear Discriminant (FLD) classifier. We applied the methods to two multisubject attention experiments: a face matching and a location matching task. We demonstrate that SVM outperforms FLD in classification performance as well as in robustness of the spatial maps obtained (i.e. discriminating volumes). In addition, the SVM discrimination maps had greater overlap with the general linear model (GLM) analysis compared to the FLD. The analysis presents two phases: during the training, the classifier algorithm finds the set of regions by which the two brain states can be best distinguished from each other. In the next phase, the test phase, given an fMRI volume from a new subject, the classifier predicts the subject's instantaneous brain state.
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              Prestimulus functional connectivity determines pain perception in humans.

              Pain is a highly subjective experience that can be substantially influenced by differences in individual susceptibility as well as personality. How susceptibility to pain and personality translate to brain activity is largely unknown. Here, we report that the functional connectivity of two key brain areas before a sensory event reflects the susceptibility to a subsequent noxious stimulus being perceived as painful. Specifically, the prestimulus connectivity among brain areas related to the subjective perception of the body and to the modulation of pain (anterior insular cortex and brainstem, respectively) determines whether a noxious event is perceived as painful. Further, these effects of prestimulus connectivity on pain perception covary with pain-relevant personality traits. More anxious and pain-attentive individuals display weaker descending connectivity to pain modulatory brain areas. We conclude that variations in functional connectivity underlie personality-related differences in individual susceptibility to pain.
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                Author and article information

                Journal
                Neuroimage
                Neuroimage
                Neuroimage
                Academic Press
                1053-8119
                1095-9572
                15 November 2012
                15 November 2012
                : 63
                : 3
                : 1162-1170
                Affiliations
                [a ]Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Nuffield Division Anaesthetics, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
                [b ]Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Wilfriedstrasse 6, CH 8032 Zurich, Switzerland
                [c ]Department of Computer Science, ETH Zurich, Universitaetstrasse 6, CH 8092 Zurich, Switzerland
                [d ]NeuroImage Nord, Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246 Hamburg, Germany
                [e ]Department of Neurology, Technische Universität München, 81675 Munich, Germany
                [f ]Wellcome Trust Centre for Neuroimaging, University College London, UK
                Author notes
                [* ]Correspondence to: K.H. Brodersen, Department of Computer Science, ETH Zurich, Universitaetstrasse 6, CH 8092 Zurich, Switzerland. Fax: + 41 4463 44 907. kay.brodersen@ 123456inf.ethz.ch
                [** ]Correspondence to: K. Wiech, Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Nuffield Division Anaesthetics, University of Oxford, Oxford, OX3 9DU, UK. Fax: + 44 1865 234541. kwiech@ 123456fmrib.ox.ac.uk
                [1]

                These authors contributed equally.

                Article
                YNIMG9716
                10.1016/j.neuroimage.2012.08.035
                3532598
                22922369
                82c723e4-5e9e-4161-b1fd-b32b3e15e793
                © 2012 Elsevier Inc.

                This document may be redistributed and reused, subject to certain conditions.

                History
                : 13 August 2012
                Categories
                Article

                Neurosciences
                classification accuracy,support vector machine,permutation test,pain,decoding
                Neurosciences
                classification accuracy, support vector machine, permutation test, pain, decoding

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