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      The Relation between Alpha/Beta Oscillations and the Encoding of Sentence induced Contextual Information

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          Abstract

          Pre-stimulus alpha (8–12 Hz) and beta (16–20 Hz) oscillations have been frequently linked to the prediction of upcoming sensory input. Do these frequency bands serve as a neural marker of linguistic prediction as well? We hypothesized that if pre-stimulus alpha and beta oscillations index language predictions, their power should monotonically relate to the degree of predictability of incoming words based on past context. We expected that the more predictable the last word of a sentence, the stronger the alpha and beta power modulation. To test this, we measured neural responses with magnetoencephalography of healthy individuals during exposure to a set of linguistically matched sentences featuring three levels of sentence context constraint (high, medium and low constraint). We observed fluctuations in alpha and beta power before last word onset, and modulations in M400 amplitude after last word onset. The M400 amplitude was monotonically related to the degree of context constraint, with a high constraining context resulting in the strongest amplitude decrease. In contrast, pre-stimulus alpha and beta power decreased more strongly for intermediate constraints, followed by high and low constraints. Therefore, unlike the M400, pre-stimulus alpha and beta dynamics were not indexing the degree of word predictability from sentence context.

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          Most cited references41

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          Dynamic imaging of coherent sources: Studying neural interactions in the human brain.

          Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept ("binding") is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto- or electroencephalography (MEG/EEG). Coherence between signals of sensors covering different scalp areas is commonly taken as a measure of functional coupling. However, this approach provides vague information on the actual cortical areas involved, owing to the complex relation between the active brain areas and the sensor recordings. We propose a solution to the crucial issue of proceeding beyond the MEG sensor level to estimate coherences between cortical areas. Dynamic imaging of coherent sources (DICS) uses a spatial filter to localize coherent brain regions and provides the time courses of their activity. Reference points for the computation of neural coupling may be based on brain areas of maximum power or other physiologically meaningful information, or they may be estimated starting from sensor coherences. The performance of DICS is evaluated with simulated data and illustrated with recordings of spontaneous activity in a healthy subject and a parkinsonian patient. Methods for estimating functional connectivities between brain areas will facilitate characterization of cortical networks involved in sensory, motor, or cognitive tasks and will allow investigation of pathological connectivities in neurological disorders.
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            The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors.

            The equation for the magnetic lead field for a given magnetoencephalography (MEG) channel is well known for arbitrary frequencies omega but is not directly applicable to MEG in the quasi-static approximation. In this paper we derive an equation for omega = 0 starting from the very definition of the lead field instead of using Helmholtz's reciprocity theorems. The results are (a) the transpose of the conductivity times the lead field is divergence-free, and (b) the lead field differs from the one in any other volume conductor by a gradient of a scalar function. Consequently, for a piecewise homogeneous and isotropic volume conductor, the lead field is always tangential at the outermost surface. Based on this theoretical result, we formulated a simple and fast method for the MEG forward calculation for one shell of arbitrary shape: we correct the corresponding lead field for a spherical volume conductor by a superposition of basis functions, gradients of harmonic functions constructed here from spherical harmonics, with coefficients fitted to the boundary conditions. The algorithm was tested for a prolate spheroid of realistic shape for which the analytical solution is known. For high order in the expansion, we found the solutions to be essentially exact and for reasonable accuracies much fewer multiplications are needed than in typical implementations of the boundary element methods. The generalization to more shells is straightforward.
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              Working memory and neural oscillations: α-γ versus θ-γ codes for distinct WM information?

              Neural oscillations at different frequencies have recently been related to a wide range of basic and higher cognitive processes. One possible role of oscillatory activity is to assure the maintenance of information in working memory (WM). Here we review the possibility that rhythmic activity at theta, alpha, and gamma frequencies serve distinct functional roles during WM maintenance. Specifically, we propose that gamma-band oscillations are generically involved in the maintenance of WM information. By contrast, alpha-band activity reflects the active inhibition of task-irrelevant information, whereas theta-band oscillations underlie the organization of sequentially ordered WM items. Finally, we address the role of cross-frequency coupling (CFC) in enabling alpha-gamma and theta-gamma codes for distinct WM information. Copyright © 2013 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                rene.terporten@mpi.nl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 December 2019
                27 December 2019
                2019
                : 9
                : 20255
                Affiliations
                [1 ]ISNI 0000 0004 0501 3839, GRID grid.419550.c, Max Planck Institute for Psycholinguistics, ; Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
                [2 ]Donders Centre for Cognitive Neuroimaging, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands
                [3 ]ISNI 0000 0004 0614 7222, GRID grid.461862.f, Lyon Neuroscience Research Center (CRNL), Brain Dynamics and Cognition Team, INSERM U1028, CNRS UMR5292, Université Claude Bernard Lyon 1, UdL, ; Lyon, France
                Author information
                http://orcid.org/0000-0003-0923-6610
                http://orcid.org/0000-0002-2692-9999
                Article
                56600
                10.1038/s41598-019-56600-x
                6934725
                31882830
                cc4bc6cd-42aa-48c2-a177-0360ccd4c1a2
                © 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 July 2019
                : 4 December 2019
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                © The Author(s) 2019

                Uncategorized
                electroencephalography - eeg,cognitive neuroscience
                Uncategorized
                electroencephalography - eeg, cognitive neuroscience

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