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      Neural encoding of the speech envelope by children with developmental dyslexia

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          Highlights

          • We measure encoding quality of low-frequency speech envelopes by children using EEG.

          • Encoding accuracy is significantly above chance for all groups.

          • Accuracy is poorer in dyslexic children than younger RL-matched children.

          • Individual differences in encoding accuracy are related to prosodic awareness.

          Abstract

          Developmental dyslexia is consistently associated with difficulties in processing phonology (linguistic sound structure) across languages. One view is that dyslexia is characterised by a cognitive impairment in the “phonological representation” of word forms, which arises long before the child presents with a reading problem. Here we investigate a possible neural basis for developmental phonological impairments. We assess the neural quality of speech encoding in children with dyslexia by measuring the accuracy of low-frequency speech envelope encoding using EEG. We tested children with dyslexia and chronological age-matched (CA) and reading-level matched (RL) younger children. Participants listened to semantically-unpredictable sentences in a word report task. The sentences were noise-vocoded to increase reliance on envelope cues. Envelope reconstruction for envelopes between 0 and 10 Hz showed that the children with dyslexia had significantly poorer speech encoding in the 0–2 Hz band compared to both CA and RL controls. These data suggest that impaired neural encoding of low frequency speech envelopes, related to speech prosody, may underpin the phonological deficit that causes dyslexia across languages.

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          Most cited references53

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          10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems.

          With the advent of multi-channel EEG hardware systems and the concurrent development of topographic and tomographic signal source localization methods, the international 10/20 system, a standard system for electrode positioning with 21 electrodes, was extended to higher density electrode settings such as 10/10 and 10/5 systems, allowing more than 300 electrode positions. However, their effectiveness as relative head-surface-based positioning systems has not been examined. We previously developed a virtual 10/20 measurement algorithm that can analyze any structural MR head and brain image. Extending this method to the virtual 10/10 and 10/5 measurement algorithms, we analyzed the MR images of 17 healthy subjects. The acquired scalp positions of the 10/10 and 10/5 systems were normalized to the Montreal Neurological Institute (MNI) stereotactic coordinates and their spatial variability was assessed. We described and examined the effects of spatial variability due to the selection of positioning systems and landmark placement strategies. As long as a detailed rule for a particular system was provided, it yielded precise landmark positions on the scalp. Moreover, we evaluated the effective spatial resolution of 329 scalp landmark positions of the 10/5 system for multi-subject studies. As long as a detailed rule for landmark setting was provided, 241 scalp positions could be set effectively when there was no overlapping of two neighboring positions. Importantly, 10/10 positions could be well separated on a scalp without overlapping. This study presents a referential framework for establishing the effective spatial resolutions of 10/20, 10/10, and 10/5 systems as relative head-surface-based positioning systems.
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            Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex.

            How natural speech is represented in the auditory cortex constitutes a major challenge for cognitive neuroscience. Although many single-unit and neuroimaging studies have yielded valuable insights about the processing of speech and matched complex sounds, the mechanisms underlying the analysis of speech dynamics in human auditory cortex remain largely unknown. Here, we show that the phase pattern of theta band (4-8 Hz) responses recorded from human auditory cortex with magnetoencephalography (MEG) reliably tracks and discriminates spoken sentences and that this discrimination ability is correlated with speech intelligibility. The findings suggest that an approximately 200 ms temporal window (period of theta oscillation) segments the incoming speech signal, resetting and sliding to track speech dynamics. This hypothesized mechanism for cortical speech analysis is based on the stimulus-induced modulation of inherent cortical rhythms and provides further evidence implicating the syllable as a computational primitive for the representation of spoken language.
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              A temporal sampling framework for developmental dyslexia.

              Neural coding by brain oscillations is a major focus in neuroscience, with important implications for dyslexia research. Here, I argue that an oscillatory 'temporal sampling' framework enables diverse data from developmental dyslexia to be drawn into an integrated theoretical framework. The core deficit in dyslexia is phonological. Temporal sampling of speech by neuroelectric oscillations that encode incoming information at different frequencies could explain the perceptual and phonological difficulties with syllables, rhymes and phonemes found in individuals with dyslexia. A conceptual framework based on oscillations that entrain to sensory input also has implications for other sensory theories of dyslexia, offering opportunities for integrating a diverse and confusing experimental literature. Copyright © 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Brain Lang
                Brain Lang
                Brain and Language
                Elsevier
                0093-934X
                1090-2155
                1 September 2016
                September 2016
                : 160
                : 1-10
                Affiliations
                Centre for Neuroscience in Education, University of Cambridge, Downing St, Cambridge CB2 3EB, UK
                Author notes
                [* ]Corresponding author at: Centre for Neuroscience in Education, Department of Psychology, Downing St., Cambridge CB2 3EB, UK.Centre for Neuroscience in EducationDepartment of PsychologyDowning St.CambridgeCB2 3EBUK ucg10@ 123456cam.ac.uk
                Article
                S0093-934X(15)30168-1
                10.1016/j.bandl.2016.06.006
                5108463
                27433986
                2580659a-30fa-4233-845d-f2645c2e636d
                © 2016 The Authors

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

                History
                : 18 November 2015
                : 11 May 2016
                : 20 June 2016
                Categories
                Article

                Neurosciences
                dyslexia,oscillations,phonology,rhythm
                Neurosciences
                dyslexia, oscillations, phonology, rhythm

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