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      Mortality threat mitigates interpersonal competition: an EEG-based hyperscanning study

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          Abstract

          Awareness of death has been shown to influence human cognition and behavior. Yet, how mortality threat (MT) impacts our daily social behavior remains elusive. To address this issue, we developed a dyadic experimental model and recruited 86 adults (43 dyads) to complete two computer-based tasks (i.e. competitive and cooperative button-pressing). We manipulated dyads’ awareness of death [MT vs neutral control (NC)] and simultaneously measured their neurophysiological activity using electroencephalography during the task. Several fundamental observations were made. First, the MT group showed significantly attenuated competition and slightly promoted cooperation. Second, compared to NC, MT significantly decreased gamma-band inter-brain synchronization (IBS) in the competitive context, which was associated with increased subjective fear of death within dyads. Notably, those effects were context-specific: we did not observe comparable results in the cooperative context. Finally, a machine-learning approach was successfully used to discriminate between the MT and NC groups based on accumulated IBS. Together, these findings indicate that MT to some extent mitigates interpersonal competition, and such mitigation might be associated with changes in gamma-band IBS.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Fitting Linear Mixed-Effects Models Using lme4

            Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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              MEG and EEG data analysis with MNE-Python

              Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.
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                Author and article information

                Contributors
                Journal
                Soc Cogn Affect Neurosci
                Soc Cogn Affect Neurosci
                scan
                Social Cognitive and Affective Neuroscience
                Oxford University Press (UK )
                1749-5016
                1749-5024
                June 2021
                23 March 2021
                23 March 2021
                : 16
                : 6
                : 621-631
                Affiliations
                departmentShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China
                departmentDepartment of Clinical Neuroscience, Karolinska Institutet , Stockholm 17165, Sweden
                departmentShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China
                departmentShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China
                departmentShanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University , Shanghai 200062, China
                Author notes
                Correspondence should be addressed to Xianchun Li, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Room 514, Junxiu Building, Shanghai 200062, China. E-mail: xcli@ 123456psy.ecnu.edu.cn .
                [*]

                Joint first authorship.

                Article
                nsab033
                10.1093/scan/nsab033
                8138089
                33755182
                2965eafb-15c5-4008-b10c-3046094f9904
                © The Author(s) 2021. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 12 July 2020
                : 21 February 2021
                : 22 March 2021
                : 15 March 2021
                Page count
                Pages: 11
                Funding
                Funded by: the Shanghai Key Base of Humanities and Social Sciences;
                Award ID: Psychology-2018
                Funded by: the Programs Foundation of Shanghai Municipal Commission of Health and Family Planning;
                Award ID: 201540114
                Funded by: Key Specialist Projects of Shanghai Municipal Commission of Health and Family Planning;
                Award ID: ZK2015B01
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 32071082 and 71942001
                Categories
                Original Manuscript
                AcademicSubjects/SCI01880

                Neurosciences
                mortality threat,competition,cooperation,hyperscanning,eeg
                Neurosciences
                mortality threat, competition, cooperation, hyperscanning, eeg

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