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      Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain

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          Abstract

          A neuroimaging study reveals how coupled brain oscillations at different frequencies align with quasi-rhythmic features of continuous speech such as prosody, syllables, and phonemes.

          Abstract

          Cortical oscillations are likely candidates for segmentation and coding of continuous speech. Here, we monitored continuous speech processing with magnetoencephalography (MEG) to unravel the principles of speech segmentation and coding. We demonstrate that speech entrains the phase of low-frequency (delta, theta) and the amplitude of high-frequency (gamma) oscillations in the auditory cortex. Phase entrainment is stronger in the right and amplitude entrainment is stronger in the left auditory cortex. Furthermore, edges in the speech envelope phase reset auditory cortex oscillations thereby enhancing their entrainment to speech. This mechanism adapts to the changing physical features of the speech envelope and enables efficient, stimulus-specific speech sampling. Finally, we show that within the auditory cortex, coupling between delta, theta, and gamma oscillations increases following speech edges. Importantly, all couplings (i.e., brain-speech and also within the cortex) attenuate for backward-presented speech, suggesting top-down control. We conclude that segmentation and coding of speech relies on a nested hierarchy of entrained cortical oscillations.

          Author Summary

          Continuous speech is organized into a nested hierarchy of quasi-rhythmic components (prosody, syllables, phonemes) with different time scales. Interestingly, neural activity in the human auditory cortex shows rhythmic modulations with frequencies that match these speech rhythms. Here, we use magnetoencephalography and information theory to study brain oscillations in participants as they process continuous speech. We show that auditory brain oscillations at different frequencies align with the rhythmic structure of speech. This alignment is more precise when participants listen to intelligible rather than unintelligible speech. The onset of speech resets brain oscillations and improves their alignment to speech rhythms; it also improves the alignment between the different frequencies of nested brain oscillations in the auditory cortex. Since these brain oscillations reflect rhythmic changes in neural excitability, they are strong candidates for mediating the segmentation of continuous speech at different time scales corresponding to key speech components such as syllables and phonemes.

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          Most cited references38

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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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            Measuring phase-amplitude coupling between neuronal oscillations of different frequencies.

            Neuronal oscillations of different frequencies can interact in several ways. There has been particular interest in the modulation of the amplitude of high-frequency oscillations by the phase of low-frequency oscillations, since recent evidence suggests a functional role for this type of cross-frequency coupling (CFC). Phase-amplitude coupling has been reported in continuous electrophysiological signals obtained from the brain at both local and macroscopic levels. In the present work, we present a new measure for assessing phase-amplitude CFC. This measure is defined as an adaptation of the Kullback-Leibler distance-a function that is used to infer the distance between two distributions-and calculates how much an empirical amplitude distribution-like function over phase bins deviates from the uniform distribution. We show that a CFC measure defined this way is well suited for assessing the intensity of phase-amplitude coupling. We also review seven other CFC measures; we show that, by some performance benchmarks, our measure is especially attractive for this task. We also discuss some technical aspects related to the measure, such as the length of the epochs used for these analyses and the utility of surrogate control analyses. Finally, we apply the measure and a related CFC tool to actual hippocampal recordings obtained from freely moving rats and show, for the first time, that the CA3 and CA1 regions present different CFC characteristics.
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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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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                December 2013
                December 2013
                31 December 2013
                : 11
                : 12
                : e1001752
                Affiliations
                [1 ]Institute for Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
                [2 ]Institute for Clinical Neuroscience and Medical Psychology, University of Düsseldorf, Düsseldorf, Germany
                [3 ]Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia @UniTn, Rovereto, Italy
                New York University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: JG NH PB SG. Performed the experiments: NH. Analyzed the data: JG NH PS SP. Contributed reagents/materials/analysis tools: SP PS GT. Wrote the paper: JG NH GT PS SP PB SG.

                Article
                PBIOLOGY-D-13-02359
                10.1371/journal.pbio.1001752
                3876971
                24391472
                ea462b3a-a61f-48d2-9fde-bcc0a38864a9
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 June 2013
                : 18 November 2013
                Page count
                Pages: 14
                Funding
                This study was supported by the Wellcome Trust (091928, 098433, 098434) and by the ESRC and MRC (RES-060-25-0010). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Computational Biology
                Natural Language Processing
                Neuroscience
                Sensory Systems
                Auditory System
                Cognitive Neuroscience
                Computational Neuroscience
                Neuroimaging

                Life sciences
                Life sciences

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