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Vicarious pain perception has been an influential paradigm for investigating the social neuroscience of empathy. This research has highlighted the importance of both shared representations (i.e., involved in both experiencing first-hand physical pain and observing pain) and mechanisms that discriminate between self and other. The majority of this research has been conducted in healthy younger adults using a group-average approach. There are, however, known inter-individual differences that can contribute to vicarious experience. One factor relates to the degree to which individuals experience reportable pain-like sensations/feelings in response to seeing others in pain. Here we conduct the first systematic investigation of the neural basis of conscious vicarious pain in a large sample of participants. Using cluster analysis, we firstly demonstrate that consciously experiencing the pain of others is surprisingly prevalent and, exists in two forms: one group experiences sensory and localised pain whilst the other group report affective and non-localised experiences. Building on this, we used electroencephalography (EEG) and structural brain imaging to examine the neural correlates of vicarious pain in the three different groups. We find that the dominant electrophysiological marker used to index vicarious pain in previous studies (mu and beta suppression) was only found to be significant in the sensory and localised pain responder group (with a sensitive null result in the 'neurotypical' group). Finally, using voxel-based morphometry (VBM) we identify a common differences in the two pain responder groups relative to typical adults; namely increased grey-matter in insula and somatosensory cortex and reduced grey matter in the right temporo-parietal junction (rTPJ). We suggest that the latter reflects a reduced ability to distinguish bodily self and other, and may be a common factor distinguishing conscious from unconscious vicarious experience.
Pubmed ID: 28759805
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Software package for analysis of brain imaging data sequences. Sequences can be a series of images from different cohorts, or time-series from same subject. Current release is designed for analysis of fMRI, PET, SPECT, EEG and MEG.
View all literature mentionsInteractive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. First developed on Matlab 5.3 under Linux, EEGLAB runs on Matlab v5 and higher under Linux, Unix, Windows, and Mac OS X (Matlab 7+ recommended). EEGLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG and other dynamic brain data using independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. EEGLAB also incorporates extensive tutorial and help windows, plus a command history function that eases users'' transition from GUI-based data exploration to building and running batch or custom data analysis scripts. EEGLAB offers a wealth of methods for visualizing and modeling event-related brain dynamics, both at the level of individual EEGLAB ''datasets'' and/or across a collection of datasets brought together in an EEGLAB ''studyset.'' For experienced Matlab users, EEGLAB offers a structured programming environment for storing, accessing, measuring, manipulating and visualizing event-related EEG data. For creative research programmers and methods developers, EEGLAB offers an extensible, open-source platform through which they can share new methods with the world research community by publishing EEGLAB ''plug-in'' functions that appear automatically in the EEGLAB menu of users who download them. For example, novel EEGLAB plug-ins might be built and released to ''pick peaks'' in ERP or time/frequency results, or to perform specialized import/export, data visualization, or inverse source modeling of EEG, MEG, and/or ECOG data. EEGLAB Features * Graphic user interface * Multiformat data importing * High-density data scrolling * Defined EEG data structure * Open source plug-in facility * Interactive plotting functions * Semi-automated artifact removal * ICA & time/frequency transforms * Many advanced plug-in toolboxes * Event & channel location handling * Forward/inverse head/source modeling
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