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      Transcranial Direct Current Stimulation Over the Left Dorsolateral Prefrontal Cortex Reduced Attention Bias Toward Negative Facial Expression: A Pilot Study in Healthy Subjects

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          Abstract

          Research in the cognitive neuroscience field has shown that individuals with a stronger attention bias for negative information had higher depression risk, which may be the underlying pathogenesis of depression. This dysfunction of affect-biased attention also represents a decline in emotion regulation ability. Clinical studies have suggested that transcranial direct current stimulation (tDCS) treatment can improve the symptoms of depression, yet the neural mechanism behind this improvement is still veiled. This study aims to investigate the effects of tDCS on affect-biased attention. A sample of healthy participants received 20 min active ( n = 22) or sham tDCS ( n = 19) over the left dorsolateral prefrontal cortex (DLPFC) for 7 consecutive days. Electroencephalographic (EEG) signals were recorded while performing the rest task and emotional oddball task. The oddball task required response to pictures of the target (positive or negative) emotional facial stimuli and neglecting distracter (negative or positive) or standard (neutral) stimuli. Welch power spectrum estimation algorithm was applied to calculate frontal alpha asymmetry (FAA) in the rest task, and the overlapping averaging method was used to extract event-related potentials (ERP) components in the oddball task. Compared to sham tDCS, active tDCS caused an obvious increment in FAA in connection with emotion regulation ( p < 0.05). Also, participants in the active tDCS group show greater P3 amplitudes following positive targets ( p < 0.05) and greater N2 amplitudes following negative distracters ( p < 0.05), reflecting emotion-related attention biases. These results offer valuable insights into the relationship between affect-biased attention and the effects of tDCS, which may be of assistance in exploring the neuropathological mechanism of depression and anxiety and new treatment strategies for tDCS.

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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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            Multidimensional Assessment of Emotion Regulation and Dysregulation: Development, Factor Structure, and Initial Validation of the Difficulties in Emotion Regulation Scale

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              Conflict monitoring and cognitive control.

              A neglected question regarding cognitive control is how control processes might detect situations calling for their involvement. The authors propose here that the demand for control may be evaluated in part by monitoring for conflicts in information processing. This hypothesis is supported by data concerning the anterior cingulate cortex, a brain area involved in cognitive control, which also appears to respond to the occurrence of conflict. The present article reports two computational modeling studies, serving to articulate the conflict monitoring hypothesis and examine its implications. The first study tests the sufficiency of the hypothesis to account for brain activation data, applying a measure of conflict to existing models of tasks shown to engage the anterior cingulate. The second study implements a feedback loop connecting conflict monitoring to cognitive control, using this to simulate a number of important behavioral phenomena.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                20 June 2022
                2022
                : 16
                : 894798
                Affiliations
                [1] 1Academy of Medical Engineering and Translational Medicine, Tianjin University , Tianjin, China
                [2] 2School of Precision Instruments and Optoelectronics Engineering, Tianjin University , Tianjin, China
                Author notes

                Edited by: Tianruo Guo, University of New South Wales, Australia

                Reviewed by: Siwei Bai, Technical University of Munich, Germany; Zhipeng Liu, Chinese Academy of Medical Sciences and Peking Union Medical College, China; Shuang Qiu, Institute of Automation (CAS), China

                *Correspondence: Dong Ming, richardming@ 123456tju.edu.cn

                This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2022.894798
                9256464
                685ba71d-81d5-473b-9efd-109638d23c69
                Copyright © 2022 Liu, Zhai, Guo, Chen, He, Ke and Ming.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 March 2022
                : 09 May 2022
                Page count
                Figures: 6, Tables: 5, Equations: 0, References: 95, Pages: 13, Words: 9786
                Funding
                Funded by: National Key Research and Development Program of China, doi 10.13039/501100012166;
                Award ID: 2017YFB1002504
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Award ID: 81925020
                Award ID: 81630051
                Categories
                Neuroscience
                Original Research

                Neurosciences
                tdcs,dlpfc,attention bias,erp,emotion regulation
                Neurosciences
                tdcs, dlpfc, attention bias, erp, emotion regulation

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