22
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Frontoparietal theta-gamma interactions track working memory enhancement with training and tDCS

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Despite considerable interest in enhancing, preserving, and rehabilitating working memory (WM), efforts to elicit sustained behavioral improvements have been met with limited success. Here, we paired WM training with transcranial direct current stimulation (tDCS) to the frontoparietal network over four days. Active tDCS enhanced WM performance by modulating interactions between frontoparietal theta oscillations and gamma activity, as measured by pre- and post-training high-density electroencephalography (EEG). Increased phase-amplitude coupling (PAC) between the prefrontal stimulation site and temporo-parietal gamma activity explained behavioral improvements, and was most effective when gamma occurred near the prefrontal theta peak. These results demonstrate for the first time that tDCS-linked WM training elicits lasting changes in behavior by optimizing the oscillatory substrates of prefrontal control.

          Related collections

          Most cited references75

          • Record: found
          • Abstract: found
          • Article: not found

          Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

          G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Nonparametric statistical testing of EEG- and MEG-data.

              In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.
                Bookmark

                Author and article information

                Journal
                9215515
                20498
                Neuroimage
                Neuroimage
                NeuroImage
                1053-8119
                1095-9572
                5 December 2020
                07 February 2020
                01 May 2020
                12 December 2020
                : 211
                : 116615
                Affiliations
                [a ]University of Nevada-Reno, Department of Psychology, Cognitive and Brain Sciences, Reno, NV, 89557, USA
                [b ]University of California-San Francisco, Department of Neurology, Neuroscape, San Francisco, CA, 94158, USA
                [c ]University of California-Berkeley, Helen Wills Neuroscience Institute, Berkeley, CA, 94720, USA
                [d ]Wayne State University, Institute of Gerontology, Life-Span Cognitive Neuroscience Program, Detroit, MI, 48202, USA
                Author notes
                [1]

                Denotes equal contribution.

                CRediT authorship contribution statement

                Kevin T. Jones: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft, Writing - review & editing, Visualization. Elizabeth L. Johnson: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing - original draft, Writing - review & editing, Visualization. Marian E. Berryhill: Conceptualization, Methodology, Resources, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition.

                [* ]Corresponding author. Department of Psychology (296), 1664 N. Virginia Street Reno, NV, 89557, USA., mberryhill@ 123456unr.edu (M.E. Berryhill).
                Article
                NIHMS1650719
                10.1016/j.neuroimage.2020.116615
                7733399
                32044440
                694a0fa4-9d7d-4dd2-9c76-c60857530ce8

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Article

                Neurosciences
                cognitive training,cross-frequency coupling,neurostimulation,prefrontal cortex,working memory

                Comments

                Comment on this article