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      Concurrent TMS-EEG to reveal the neuroplastic changes in the prefrontal and insular cortices in the analgesic effects of DLPFC-rTMS

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      Cerebral Cortex
      Oxford University Press (OUP)

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          Abstract

          The dorsolateral prefrontal cortex (DLPFC) is an important target for repetitive transcranial magnetic stimulation (rTMS) to reduce pain. However, the analgesic efficacy of DLPFC-rTMS needs to be optimized, in which the mechanisms of action remain unclear. Concurrent TMS and electroencephalogram (TMS-EEG) is able to evaluate neuroplastic changes beyond the motor cortex. Using TMS-EEG, this study was designed to investigate the local and distributed neuroplastic changes associated with DLPFC analgesia. Thirty-four healthy adults received DLPFC or sham stimulation in a randomized, crossover design. In each session, participants underwent cold pain and TMS-EEG assessment both before and after 10-Hz rTMS. We provide novel findings that DLPFC analgesia is associated with a smaller N120 amplitude in the contralateral prefrontal cortex as well as with a larger N120 peak in the ipsilateral insular cortex. Furthermore, there was a strong negative correlation between N120 changes of these two regions whereby the amplitude changes of this dyad were associated with increased pain threshold. In addition, DLPFC stimulation enhanced coherence between the prefrontal and somatosensory cortices oscillating in the gamma frequency. Overall, our data present novel evidence on local and distributed neuroplastic changes associated with DLPFC analgesia.

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          • Record: found
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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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            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.
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                Author and article information

                Contributors
                Journal
                Cerebral Cortex
                Oxford University Press (OUP)
                1047-3211
                1460-2199
                October 15 2022
                October 08 2022
                January 20 2022
                October 15 2022
                October 08 2022
                January 20 2022
                : 32
                : 20
                : 4436-4446
                Article
                10.1093/cercor/bhab493
                35059703
                4af0ed8f-f2e0-416b-84f7-c997e63cef50
                © 2022

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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