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      Temporal integration as “common currency” of brain and self scale‐free activity in resting‐state EEG correlates with temporal delay effects on self‐relatedness

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          Abstract

          The self is a multifaceted phenomenon that integrates information and experience across multiple time scales. How temporal integration on the psychological level of the self is related to temporal integration on the neuronal level remains unclear. To investigate temporal integration on the psychological level, we modified a well‐established self‐matching paradigm by inserting temporal delays. On the neuronal level, we indexed temporal integration in resting‐state EEG by two related measures of scale‐free dynamics, the power law exponent and autocorrelation window. We hypothesized that the previously established self‐prioritization effect, measured as decreased response times or increased accuracy for self‐related stimuli, would change with the insertion of different temporal delays between the paired stimuli, and that these changes would be related to temporal integration on the neuronal level. We found a significant self‐prioritization effect on accuracy in all conditions with delays, indicating stronger temporal integration of self‐related stimuli. Further, we observed a relationship between temporal integration on psychological and neuronal levels: higher degrees of neuronal integration, that is, higher power‐law exponent and longer autocorrelation window, during resting‐state EEG were related to a stronger increase in the self‐prioritization effect across longer temporal delays. We conclude that temporal integration on the neuronal level serves as a template for temporal integration of the self on the psychological level. Temporal integration can thus be conceived as the “common currency” of neuronal and psychological levels of self.

          Abstract

          The self‐integrates information and experience across multiple time scales. We here provide evidence for a relationship between temporal integration on both neuronal and psychological levels. Specifically, neuronal indices of temporal integration, power‐law exponent, and autocorrelation window, relate to psychological measures of temporal integration with respect to self. We therefore propose that temporal integration on the neuronal level serves as template or blueprint for temporal integration on the psychological level—temporal integration may serve as “common currency” of brain and self.

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          Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain.

          This paper presents a new method for localizing the electric activity in the brain based on multichannel surface EEG recordings. In contrast to the models presented up to now the new method does not assume a limited number of dipolar point sources nor a distribution on a given known surface, but directly computes a current distribution throughout the full brain volume. In order to find a unique solution for the 3-dimensional distribution among the infinite set of different possible solutions, the method assumes that neighboring neurons are simultaneously and synchronously activated. The basic assumption rests on evidence from single cell recordings in the brain that demonstrates strong synchronization of adjacent neurons. In view of this physiological consideration the computational task is to select the smoothest of all possible 3-dimensional current distributions, a task that is a common procedure in generalized signal processing. The result is a true 3-dimensional tomography with the characteristic that localization is preserved with a certain amount of dispersion, i.e., it has a relatively low spatial resolution. The new method, which we call Low Resolution Electromagnetic Tomography (LORETA) is illustrated with two different sets of evoked potential data, the first showing the tomography of the P100 component to checkerboard stimulation of the left, right, upper and lower hemiretina, and the second showing the results for the auditory N100 component and the two cognitive components CNV and P300. A direct comparison of the tomography results with those obtained from fitting one and two dipoles illustrates that the new method provides physiologically meaningful results while dipolar solutions fail in many situations. In the case of the cognitive components, the method offers new hypotheses on the location of higher cognitive functions in the brain.
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            Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the Archives of General Psychiatry.

            The analysis of repeated-measures data presents challenges to investigators and is a topic for ongoing discussion in the Archives of General Psychiatry. Traditional methods of statistical analysis (end-point analysis and univariate and multivariate repeated-measures analysis of variance [rANOVA and rMANOVA, respectively]) have known disadvantages. More sophisticated mixed-effects models provide flexibility, and recently developed software makes them available to researchers. To review methods for repeated-measures analysis and discuss advantages and potential misuses of mixed-effects models. Also, to assess the extent of the shift from traditional to mixed-effects approaches in published reports in the Archives of General Psychiatry. The Archives of General Psychiatry from 1989 through 2001, and the Department of Veterans Affairs Cooperative Study 425. Studies with a repeated-measures design, at least 2 groups, and a continuous response variable. The first author ranked the studies according to the most advanced statistical method used in the following order: mixed-effects model, rMANOVA, rANOVA, and end-point analysis. The use of mixed-effects models has substantially increased during the last 10 years. In 2001, 30% of clinical trials reported in the Archives of General Psychiatry used mixed-effects analysis. Repeated-measures ANOVAs continue to be used widely for the analysis of repeated-measures data, despite risks to interpretation. Mixed-effects models use all available data, can properly account for correlation between repeated measurements on the same subject, have greater flexibility to model time effects, and can handle missing data more appropriately. Their flexibility makes them the preferred choice for the analysis of repeated-measures data.
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              The future of memory: remembering, imagining, and the brain.

              During the past few years, there has been a dramatic increase in research examining the role of memory in imagination and future thinking. This work has revealed striking similarities between remembering the past and imagining or simulating the future, including the finding that a common brain network underlies both memory and imagination. Here, we discuss a number of key points that have emerged during recent years, focusing in particular on the importance of distinguishing between temporal and nontemporal factors in analyses of memory and imagination, the nature of differences between remembering the past and imagining the future, the identification of component processes that comprise the default network supporting memory-based simulations, and the finding that this network can couple flexibly with other networks to support complex goal-directed simulations. This growing area of research has broadened our conception of memory by highlighting the many ways in which memory supports adaptive functioning. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                i.r.kolvoort@uva.nl
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                22 July 2020
                15 October 2020
                : 41
                : 15 ( doiID: 10.1002/hbm.v41.15 )
                : 4355-4374
                Affiliations
                [ 1 ] Mind, Brain Imaging and Neuroethics Unit, Institute of Mental Health Research University of Ottawa Ottawa Ontario Canada
                [ 2 ] Department of Psychology, Programme Group Psychological Methods University of Amsterdam Amsterdam The Netherlands
                Author notes
                [*] [* ] Correspondence

                Ivar R. Kolvoort, Department of Psychology, Programme group Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129, room number S.18, P.O. Box 15906, 1018 WS, 1001 NK, Amsterdam, The Netherlands.

                Email: i.r.kolvoort@ 123456uva.nl

                Author information
                https://orcid.org/0000-0002-7072-4392
                Article
                HBM25129
                10.1002/hbm.25129
                7502844
                32697351
                ee1edc05-1472-4802-b114-baae5ac812a7
                © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 February 2020
                : 01 June 2020
                : 24 June 2020
                Page count
                Figures: 7, Tables: 3, Pages: 20, Words: 14975
                Funding
                Funded by: Horizon 2020 Framework Programme , open-funder-registry 10.13039/100010661;
                Award ID: 785907
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                October 15, 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.1 mode:remove_FC converted:21.09.2020

                Neurology
                common currency,eeg,scale‐free activity,self,self‐prioritization effect,self‐relatedness,temporal integration

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