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      Probing regional cortical excitability via input–output properties using transcranial magnetic stimulation and electroencephalography coupling

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          Abstract

          The modular organization of the cortex refers to subsets of highly interconnected nodes, sharing specific cytoarchitectural and dynamical properties. These properties condition the level of excitability of local pools of neurons. In this study, we described TMS evoked potentials (TEP) input–output properties to provide new insights into regional cortical excitability. We combined robotized TMS with EEG to disentangle region‐specific TEP from threshold to saturation and describe their oscillatory contents. Twenty‐two young healthy participants received robotized TMS pulses over the right primary motor cortex (M1), the right dorsolateral prefrontal cortex (DLPFC) and the right superior occipital lobe (SOL) at five stimulation intensities (40, 60, 80, 100, and 120% resting motor threshold) and one short‐interval intracortical inhibition condition during EEG recordings. Ten additional subjects underwent the same experiment with a realistic sham TMS procedure. The results revealed interregional differences in the TEPs input–output functions as well as in the responses to paired‐pulse conditioning protocols, when considering early local components (<80 ms). Each intensity in the three regions was associated with complex patterns of oscillatory activities. The quality of the regression of TEPs over stimulation intensity was used to derive a new readout for cortical excitability and dynamical properties, revealing lower excitability in the DLPFC, followed by SOL and M1. The realistic sham experiment confirmed that these early local components were not contaminated by multisensory stimulations. This study provides an entirely new analytic framework to characterize input–output relations throughout the cortex, paving the way to a more accurate definition of local cortical excitability.

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          OpenMEEG: opensource software for quasistatic bioelectromagnetics

          Background Interpreting and controlling bioelectromagnetic phenomena require realistic physiological models and accurate numerical solvers. A semi-realistic model often used in practise is the piecewise constant conductivity model, for which only the interfaces have to be meshed. This simplified model makes it possible to use Boundary Element Methods. Unfortunately, most Boundary Element solutions are confronted with accuracy issues when the conductivity ratio between neighboring tissues is high, as for instance the scalp/skull conductivity ratio in electro-encephalography. To overcome this difficulty, we proposed a new method called the symmetric BEM, which is implemented in the OpenMEEG software. The aim of this paper is to present OpenMEEG, both from the theoretical and the practical point of view, and to compare its performances with other competing software packages. Methods We have run a benchmark study in the field of electro- and magneto-encephalography, in order to compare the accuracy of OpenMEEG with other freely distributed forward solvers. We considered spherical models, for which analytical solutions exist, and we designed randomized meshes to assess the variability of the accuracy. Two measures were used to characterize the accuracy. the Relative Difference Measure and the Magnitude ratio. The comparisons were run, either with a constant number of mesh nodes, or a constant number of unknowns across methods. Computing times were also compared. Results We observed more pronounced differences in accuracy in electroencephalography than in magnetoencephalography. The methods could be classified in three categories: the linear collocation methods, that run very fast but with low accuracy, the linear collocation methods with isolated skull approach for which the accuracy is improved, and OpenMEEG that clearly outperforms the others. As far as speed is concerned, OpenMEEG is on par with the other methods for a constant number of unknowns, and is hence faster for a prescribed accuracy level. Conclusions This study clearly shows that OpenMEEG represents the state of the art for forward computations. Moreover, our software development strategies have made it handy to use and to integrate with other packages. The bioelectromagnetic research community should therefore be able to benefit from OpenMEEG with a limited development effort.
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            TMS and drugs revisited 2014.

            The combination of pharmacology and transcranial magnetic stimulation to study the effects of drugs on TMS-evoked EMG responses (pharmaco-TMS-EMG) has considerably improved our understanding of the effects of TMS on the human brain. Ten years have elapsed since an influential review on this topic has been published in this journal (Ziemann, 2004). Since then, several major developments have taken place: TMS has been combined with EEG to measure TMS evoked responses directly from brain activity rather than by motor evoked potentials in a muscle, and pharmacological characterization of the TMS-evoked EEG potentials, although still in its infancy, has started (pharmaco-TMS-EEG). Furthermore, the knowledge from pharmaco-TMS-EMG that has been primarily obtained in healthy subjects is now applied to clinical settings, for instance, to monitor or even predict clinical drug responses in neurological or psychiatric patients. Finally, pharmaco-TMS-EMG has been applied to understand the effects of CNS active drugs on non-invasive brain stimulation induced long-term potentiation-like and long-term depression-like plasticity. This is a new field that may help to develop rationales of pharmacological treatment for enhancement of recovery and re-learning after CNS lesions. This up-dated review will highlight important knowledge and recent advances in the contribution of pharmaco-TMS-EMG and pharmaco-TMS-EEG to our understanding of normal and dysfunctional excitability, connectivity and plasticity of the human brain. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
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              Natural frequencies of human corticothalamic circuits.

              The frequency tuning of a system can be directly determined by perturbing it and by observing the rate of the ensuing oscillations, the so called natural frequency. This approach is used, for example, in physics, in geology, and also when one tunes a musical instrument. In the present study, we employ transcranial magnetic stimulation (TMS) to directly perturb a set of selected corticothalamic modules (Brodmann areas 19, 7, and 6) and high-density electroencephalogram to measure their natural frequency. TMS consistently evoked dominant alpha-band oscillations (8-12 Hz) in the occipital cortex, beta-band oscillations (13-20 Hz) in the parietal cortex, and fast beta/gamma-band oscillations (21-50 Hz) in the frontal cortex. Each cortical area tended to preserve its own natural frequency also when indirectly engaged by TMS through brain connections and when stimulated at different intensities, indicating that the observed oscillations reflect local physiological mechanisms. These findings were reproducible across individuals and represent the first direct characterization of the coarse electrophysiological properties of three associative areas of the human cerebral cortex. Most importantly, they indicate that, in healthy subjects, each corticothalamic module is normally tuned to oscillate at a characteristic rate. The natural frequency can be directly measured in virtually any area of the cerebral cortex and may represent a straightforward and flexible way to probe the state of human thalamocortical circuits at the patient's bedside.
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                Author and article information

                Contributors
                estelle.raffin@epfl.ch
                sylvain.harquel@univ-grenoble-alpes.fr
                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
                07 May 2020
                July 2020
                : 41
                : 10 ( doiID: 10.1002/hbm.v41.10 )
                : 2741-2761
                Affiliations
                [ 1 ] University of Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences Grenoble France
                [ 2 ] Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL) Geneva Switzerland
                [ 3 ] Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation Sion Switzerland
                [ 4 ] CNRS, UMR5105, Laboratoire Psychologie et NeuroCognition, LPNC University of Grenoble Alpes Grenoble France
                [ 5 ] University of Grenoble‐Alpes, CNRS, CHU Grenoble Alpes, INSERM, CNRS, IRMaGe Grenoble France
                Author notes
                [*] [* ] Correspondence

                Estelle Raffin, Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), Chemin des Mines 9, 1202 Geneva, Switzerland.

                Email: estelle.raffin@ 123456epfl.ch

                Sylvain Harquel, LPNC—Laboratoire de Psychologie et NeuroCognition, CNRS UMR 5105—UGA, BSHM—1251 Av Centrale, CS40700, 38058 Grenoble Cedex 9, France.

                Email: sylvain.harquel@ 123456univ-grenoble-alpes.fr

                Author information
                https://orcid.org/0000-0003-3262-5251
                https://orcid.org/0000-0001-8756-2230
                https://orcid.org/0000-0003-0776-0216
                Article
                HBM24975
                10.1002/hbm.24975
                7294059
                32379389
                40d57dbb-3c45-4d83-9801-22f5e881c477
                © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 31 August 2019
                : 04 February 2020
                : 23 February 2020
                Page count
                Figures: 7, Tables: 0, Pages: 21, Words: 17246
                Funding
                Funded by: France Life Imaging Network
                Award ID: ANR‐11‐INBS‐0006
                Funded by: NeuroCoG IDEX UGA
                Award ID: ANR‐15‐IDEX‐02
                Funded by: Oscilloscopus
                Award ID: ANR‐15‐CE37‐0015‐01
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                July 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.4 mode:remove_FC converted:15.06.2020

                Neurology
                cortical excitability,input–output functions,linear regression,tms–eeg
                Neurology
                cortical excitability, input–output functions, linear regression, tms–eeg

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