Trial reportPloS one2013
Suppression of the µ rhythm during speech and non-speech discrimination revealed by independent component analysis: implications for sensorimotor integration in speech processing.
Trial report in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Sex differences in early sensorimotor processing for speech discrimination.Scientific reports · 2019Trial
- Beta rhythm modulation by speech sounds: somatotopic mapping in somatosensory cortex.Scientific reports · 2016Trial
- Speech Perception and Preparation Are Supported by Distinct Neural Dynamics Across Development.Neurobiology of language (Cambridge, Mass.) · 2026Article
- Human Auditory-Motor Networks Show Frequency-Specific Phase-Based Coupling in Resting-State MEG.Human brain mapping · 2025Article
- Green tea consumption and the management of adrenal stress hormones in adolescents who stutter.Biomedical reports · 2022Article
- A State-of-the-Art Review of EEG-Based Imagined Speech Decoding.Frontiers in human neuroscience · 2022Review
- Motor engagement relates to accurate perception of phonemes and audiovisual words, but not auditory words.Communications biology · 2021Article
- Article
- Power and phase coherence in sensorimotor mu and temporal lobe alpha components during covert and overt syllable production.Experimental brain research · 2019Article
- Involvement of the Cortico-Basal Ganglia-Thalamocortical Loop in Developmental Stuttering.Frontiers in psychology · 2019Review
- The Application of EEG Mu Rhythm Measures to Neurophysiological Research in Stuttering.Frontiers in human neuroscience · 2019Article
- Trait related sensorimotor deficits in people who stutter: An EEG investigation ofNeuroImage. Clinical · 2018Article
- The Effects of Fluency Enhancing Conditions on Sensorimotor Control of Speech in Typically Fluent Speakers: An EEG Mu Rhythm Study.Frontiers in human neuroscience · 2018Article
- Time-Frequency Analysis of Mu Rhythm Activity during Picture and Video Action Naming Tasks.Brain sciences · 2017Article
- EEG Mu (µ) rhythm spectra and oscillatory activity differentiate stuttering from non-stuttering adults.NeuroImage · 2017Article
- The interpretation of mu suppression as an index of mirror neuron activity: past, present and future.Royal Society open science · 2017Review
- Sensorimotor Oscillations Prior to Speech Onset Reflect Altered Motor Networks in Adults Who Stutter.Frontiers in human neuroscience · 2016Article
- The plasticity of the mirror system: how reward learning modulates cortical motor simulation of others.Neuropsychologia · 2015Article
- Auditory cortical deactivation during speech production and following speech perception: an EEG investigation of the temporal dynamics of the auditory alpha rhythm.Frontiers in human neuroscience · 2015Article
- Temporal dynamics of sensorimotor integration in speech perception and production: independent component analysis of EEG data.Frontiers in psychology · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundConstructivist theories propose that articulatory hypotheses about incoming phonetic targets may function to enhance perception by limiting the possibilities for sensory analysis. To provide evidence for this proposal, it is necessary to map ongoing, high-temporal resolution changes in sensorimotor activity (i.e., the sensorimotor μ rhythm) to accurate speech and non-speech discrimination performance (i.e., correct trials.).
methodsSixteen participants (15 female and 1 male) were asked to passively listen to or actively identify speech and tone-sweeps in a two-force choice discrimination task while the electroencephalograph (EEG) was recorded from 32 channels. The stimuli were presented at signal-to-noise ratios (SNRs) in which discrimination accuracy was high (i.e., 80-100%) and low SNRs producing discrimination performance at chance. EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB.
resultsICA revealed left and right sensorimotor µ components for 14/16 and 13/16 participants respectively that were identified on the basis of scalp topography, spectral peaks, and localization to the precentral and postcentral gyri. Time-frequency analysis of left and right lateralized µ component clusters revealed significant (pFDR<.05) suppression in the traditional beta frequency range (13-30 Hz) prior to, during, and following syllable discrimination trials. No significant differences from baseline were found for passive tasks. Tone conditions produced right µ beta suppression following stimulus onset only. For the left µ, significant differences in the magnitude of beta suppression were found for correct speech discrimination trials relative to chance trials following stimulus offset.
conclusionsFindings are consistent with constructivist, internal model theories proposing that early forward motor models generate predictions about likely phonemic units that are then synthesized with incoming sensory cues during active as opposed to passive processing. Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.