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      Increased similarity of neural responses to experienced and empathic distress in costly altruism

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          Abstract

          Empathy—affective resonance with others’ sensory or emotional experiences—is hypothesized to be an important precursor to altruism. However, it is not known whether real-world altruists’ heightened empathy reflects true self-other mapping of multi-voxel neural response patterns. We investigated this relationship in adults who had engaged in extraordinarily costly real-world altruism: donating a kidney to a stranger. Altruists and controls completed fMRI testing while anticipating and experiencing pain, and watching as a stranger anticipated and experienced pain. Machine learning classifiers tested for shared representation between experienced and observed distress. Altruists exhibited more similar representations of experienced and observed fearful anticipation spontaneously and following an empathy prompt in anterior insula and anterior/middle cingulate cortex, respectively, suggesting heightened empathic proclivities and abilities for fear. During pain epochs, altruists were distinguished by spontaneous empathic responses in anterior insula, anterior/mid-cingulate cortex and supplementary motor area, but showed no difference from controls after the empathy prompt. These findings (1) link shared multi-voxel representations of the distress of self and others to real-world costly altruism, (2) reinforce distinctions between empathy for sensory states like pain and anticipatory affective states like fear, and (3) highlight the importance of differentiating between the proclivity and ability to empathize.

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          Most cited references51

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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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            Pain and emotion interactions in subregions of the cingulate gyrus.

            Brent Vogt (2005)
            Acute pain and emotion are processed in two forebrain networks, and the cingulate cortex is involved in both. Although Brodmann's cingulate gyrus had two divisions and was not based on any functional criteria, functional imaging studies still use this model. However, recent cytoarchitectural studies of the cingulate gyrus support a four-region model, with subregions, that is based on connections and qualitatively unique functions. Although the activity evoked by pain and emotion has been widely reported, some view them as emergent products of the brain rather than of small aggregates of neurons. Here, we assess pain and emotion in each cingulate subregion, and assess whether pain is co-localized with negative affect. Amazingly, these activation patterns do not simply overlap.
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              On the interpretation of weight vectors of linear models in multivariate neuroimaging.

              The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                kmo52@georgetown.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                24 July 2019
                24 July 2019
                2019
                : 9
                : 10774
                Affiliations
                [1 ]ISNI 0000 0001 1955 1644, GRID grid.213910.8, Interdisciplinary Program in Neuroscience, , Georgetown University, ; Washington, DC 20057 USA
                [2 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Psychology, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [3 ]ISNI 0000 0001 1955 1644, GRID grid.213910.8, Department of Psychology, , Georgetown University, ; Washington, DC 20057 USA
                [4 ]ISNI 0000 0001 2297 5165, GRID grid.94365.3d, National Institute of Nursing Research, , National Institutes of Health, ; Bethesda, MD 20892 USA
                [5 ]ISNI 0000 0001 2186 0438, GRID grid.411667.3, Department of Neurology, , Georgetown University Medical Center, ; Washington, DC 20057 USA
                Author information
                http://orcid.org/0000-0002-9649-9007
                http://orcid.org/0000-0003-0429-4598
                http://orcid.org/0000-0003-1350-9458
                Article
                47196
                10.1038/s41598-019-47196-3
                6656917
                31341206
                6309f386-b06a-406d-bbef-4b41f53cb9c1
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 February 2019
                : 4 July 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100006108, U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS);
                Award ID: TL1TR001431
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000925, John Templeton Foundation (JTF);
                Award ID: 47861
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                empathy,human behaviour
                Uncategorized
                empathy, human behaviour

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