4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Electric field dynamics in the brain during multi-electrode transcranial electric stimulation

      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

          Neural oscillations play a crucial role in communication between remote brain areas. Transcranial electric stimulation with alternating currents (TACS) can manipulate these brain oscillations in a non-invasive manner. Recently, TACS using multiple electrodes with phase shifted stimulation currents were developed to alter long-range connectivity. Typically, an increase in coordination between two areas is assumed when they experience an in-phase stimulation and a disorganization through an anti-phase stimulation. However, the underlying biophysics of multi-electrode TACS has not been studied in detail. Here, we leverage direct invasive recordings from two non-human primates during multi-electrode TACS to characterize electric field magnitude and phase as a function of the phase of stimulation currents. Further, we report a novel “traveling wave” stimulation where the location of the electric field maximum changes over the stimulation cycle. Our results provide a mechanistic understanding of the biophysics of multi-electrode TACS and enable future developments of novel stimulation protocols.

          Abstract

          Transcranial alternating current stimulation (TACS) of the brain is widely used in neuroscience, but the electric fields produced when multiple stimulation electrodes are used are not well understood. Here, the authors directly record electric fields in primate brains during multi-electrode TACS.

          Related collections

          Most cited references66

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

          FSL.

          FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            FreeSurfer.

            FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
              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

                Author and article information

                Contributors
                aopitz@umn.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                12 June 2019
                12 June 2019
                2019
                : 10
                : 2573
                Affiliations
                [1 ]ISNI 0000000419368657, GRID grid.17635.36, Department of Biomedical Engineering, , University of Minnesota, ; Minneapolis, 55455 MN USA
                [2 ]ISNI 0000 0001 2189 4777, GRID grid.250263.0, Center for Biomedical Imaging and Neuromodulation, , Nathan Kline Institute for Psychiatric Research, ; Orangeburg, 10962 NY USA
                [3 ]GRID grid.428122.f, Center for the Developing Brain, , Child Mind Institute, ; New York, 10022 NY USA
                [4 ]ISNI 0000000419368729, GRID grid.21729.3f, Departments of Neurological Surgery and Psychiatry, , Columbia University College of Physicians and Surgeons, ; New York, 10032 NY USA
                Author information
                http://orcid.org/0000-0002-2205-4910
                Article
                10581
                10.1038/s41467-019-10581-7
                6561925
                31189931
                f05806c6-2640-4a68-87c4-0eb8dde310ea
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 June 2018
                : 20 May 2019
                Funding
                Funded by: University of Minnesota’s MnDRIVE Initiative
                Funded by: FundRef https://doi.org/10.13039/100000025, U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH);
                Award ID: R01 MH111439-01
                Award ID: R21 MH110217-01
                Award ID: R01 MH111439-01
                Award ID: P50 MH109429
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                electrophysiology,biophysics,neuroscience,biophysical models
                Uncategorized
                electrophysiology, biophysics, neuroscience, biophysical models

                Comments

                Comment on this article