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      Sensorimotor activation related to speaker vs. listener role during natural conversation

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          Highlights

          • Simultaneous MEG recordings from two persons during live interaction.

          • Left-lateralized involvement of sensorimotor cortex during natural conversation.

          • Phasic modulation of sensorimotor rhythm indexing preparation to own speaking turn.

          Abstract

          Although the main function of speech is communication, the brain bases of speaking and listening are typically studied in single subjects, leaving unsettled how brain function supports interactive vocal exchange. Here we used whole-scalp magnetoencephalography (MEG) to monitor modulation of sensorimotor brain rhythms related to the speaker vs. listener roles during natural conversation.

          Nine dyads of healthy adults were recruited. The partners of a dyad were engaged in live conversations via an audio link while their brain activity was measured simultaneously in two separate MEG laboratories.

          The levels of ∼10-Hz and ∼20-Hz rolandic oscillations depended on the speaker vs. listener role. In the left rolandic cortex, these oscillations were consistently (by ∼20%) weaker during speaking than listening. At the turn changes in conversation, the level of the ∼10 Hz oscillations enhanced transiently around 1.0 or 2.3 s before the end of the partner’s turn.

          Our findings indicate left-hemisphere-dominant involvement of the sensorimotor cortex during own speech in natural conversation. The ∼10-Hz modulations could be related to preparation for starting one’s own turn, already before the partner’s turn has finished.

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          Most cited references28

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          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.
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            Human cortical oscillations: a neuromagnetic view through the skull.

            The mammalian cerebral cortex generates a variety of rhythmic oscillations, detectable directly from the cortex or the scalp. Recent non-invasive recordings from intact humans, by means of neuromagnetometers with large sensor arrays, have shown that several regions of the healthy human cortex have their own intrinsic rhythms, typically 8-40 Hz in frequency, with modality- and frequency-specific reactivity. The conventional hypotheses about the functional significance of brain rhythms extend from epiphenomena to perceptual binding and object segmentation. Recent data indicate that some cortical rhythms can be related to periodic activity of peripheral sensor and effector organs.
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              Removal of magnetoencephalographic artifacts with temporal signal-space separation: demonstration with single-trial auditory-evoked responses.

              Magnetic interference signals often hamper analysis of magnetoencephalographic (MEG) measurements. Artifact sources in the proximity of the sensors cause strong and spatially complex signals that are particularly challenging for the existing interference-suppression methods. Here we demonstrate the performance of the temporally extended signal space separation method (tSSS) in removing strong interference caused by external and nearby sources on auditory-evoked magnetic fields-the sources of which are well established. The MEG signals were contaminated by normal environmental interference, by artificially produced additional external interference, and by nearby artifacts produced by a piece of magnetized wire in the subject's lip. After tSSS processing, even the single-trial auditory responses had a good-enough signal-to-noise ratio for detailed waveform and source analysis. Waveforms and source locations of the tSSS-reconstructed data were in good agreement with the responses from the control condition without extra interference. Our results demonstrate that tSSS is a robust and efficient method for removing a wide range of different types of interference signals in neuromagnetic multichannel measurements. (c) 2008 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                Journal
                Neurosci Lett
                Neurosci. Lett
                Neuroscience Letters
                Elsevier Scientific Publishers Ireland
                0304-3940
                1872-7972
                12 February 2016
                12 February 2016
                : 614
                : 99-104
                Affiliations
                [0005]Department of Neuroscience and Biomedical Engineering & the MEG Core, Aalto NeuroImaging, School of Science, Aalto University, Finland
                Author notes
                [* ]Corresponding author. Fax: +358 9 470 22969. anne.mandel@ 123456aalto.fi
                [1]

                Present address: BCBL, Basque Center on Cognition, Brain and Language, San Sebastian, Spain.

                Article
                S0304-3940(15)30345-1
                10.1016/j.neulet.2015.12.054
                4756274
                26742643
                cfa752f7-51b7-4597-99c3-c3a74f24ab16
                © 2015 The Authors

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

                History
                : 30 September 2015
                : 1 December 2015
                : 24 December 2015
                Categories
                Research Paper

                Neurosciences
                meg, magnetoencephalography,tfr, time–frequency representation,sensorimotor activation,mu rhythm,conversation,magnetoencephalography

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