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      Visual perspective taking neural processing in forensic cases with high density EEG

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          Abstract

          This EEG study aims at dissecting the differences in the activation of neural generators between borderline personality disorder patients with court-ordered measures (BDL-COM) and healthy controls in visual perspective taking. We focused on the distinction between mentalizing (Avatar) and non-mentalizing (Arrow) stimuli as well as self versus other-perspective in the dot perspective task (dPT) in a sample of 15 BDL-COM cases and 54 controls, all of male gender. BDL-COM patients showed a late and diffuse right hemisphere involvement of neural generators contrasting with the occipitofrontal topography observed in controls. For Avatars only and compared to controls, the adoption of Self perspective involved a lower EEG activity in the left inferior frontal, right middle temporal cortex and insula in BDL-COM patients prior to 80 ms post-stimulus. When taking the Other-perspective, BDL-COM patients also showed a lower activation of superior frontal, right inferior temporal and fusiform cortex within the same time frame. The beta oscillation power was significantly lower in BDL-COM patients than controls between 400 and 1300 ms post stimulus in the Avatar-Other condition. These results indicate that BDL-COM patients display both altered topography of EEG activation patterns and reduced abilities to mobilize beta oscillations during the treatment of mentalistic stimuli in dPT.

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          Most cited references66

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

            This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
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              The functional architecture of human empathy.

              Empathy accounts for the naturally occurring subjective experience of similarity between the feelings expressed by self and others without loosing sight of whose feelings belong to whom. Empathy involves not only the affective experience of the other person's actual or inferred emotional state but also some minimal recognition and understanding of another's emotional state. In light of multiple levels of analysis ranging from developmental psychology, social psychology, cognitive neuroscience, and clinical neuropsychology, this article proposes a model of empathy that involves parallel and distributed processing in a number of dissociable computational mechanisms. Shared neural representations, self-awareness, mental flexibility, and emotion regulation constitute the basic macrocomponents of empathy, which are underpinned by specific neural systems. This functional model may be used to make specific predictions about the various empathy deficits that can be encountered in different forms of social and neurological disorders.
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                Author and article information

                Contributors
                vincent.rochas@fcbg.ch
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 July 2024
                10 July 2024
                2024
                : 14
                : 15973
                Affiliations
                [1 ]Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, ( https://ror.org/01swzsf04) Geneva, Switzerland
                [2 ]Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland
                [3 ]Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, ( https://ror.org/01swzsf04) Geneva, Switzerland
                [4 ]GRID grid.150338.c, ISNI 0000 0001 0721 9812, Division of Institutional Measures, Medical Direction, , Geneva University Hospitals, ; Geneva, Switzerland
                [5 ]Faculty of Medicine of the University of Geneva, ( https://ror.org/01swzsf04) Geneva, Switzerland
                [6 ]School of Psychology, University of Queensland, ( https://ror.org/00rqy9422) Brisbane, Australia
                Article
                66522
                10.1038/s41598-024-66522-y
                11237136
                38987366
                582064d5-cf41-407a-88e5-10abb72c398f
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 February 2024
                : 2 July 2024
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                © Springer Nature Limited 2024

                Uncategorized
                empathy,perception
                Uncategorized
                empathy, perception

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