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Suppression of the µ rhythm during speech and non-speech discrimination revealed by independent component analysis: implications for sensorimotor integration in speech processing.

Bowers A, Saltuklaroglu T, Harkrider A, Cuellar M - PLoS ONE (2013)

Bottom Line: EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB.ICA 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.Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Communication Disorders, University of Arkansas, Fayetteville, Arkansas, United States of America.

ABSTRACT

Background: Constructivist 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.).

Methods: Sixteen 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.

Results: ICA 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.

Conclusions: Findings 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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Related in: MedlinePlus

Mean left and right hemisphere µ time-frequency ERSPs (event-related spectral perturbations).ERSPs are scaled in the same root-mean-square decibel units as a function of condition (1×7) and random effects analysis indicating significant values in the traditional beta (13–30 Hz) and alpha ranges (8–13 Hz). Non-significant values are colored green, with significant values shown in orange and red. Event-related decreases in spectral power are indicated in blue (−4.5) and increases are indicated in red (4.5).
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pone-0072024-g005: Mean left and right hemisphere µ time-frequency ERSPs (event-related spectral perturbations).ERSPs are scaled in the same root-mean-square decibel units as a function of condition (1×7) and random effects analysis indicating significant values in the traditional beta (13–30 Hz) and alpha ranges (8–13 Hz). Non-significant values are colored green, with significant values shown in orange and red. Event-related decreases in spectral power are indicated in blue (−4.5) and increases are indicated in red (4.5).

Mentions: Left and Right μ mean ERSP values across subjects and conditions are shown in a time-frequency map with corrected significance values for condition in a separate map (Figure 5). Non-significant values are depicted in green and significant values are depicted in color from orange for weaker values to red for stronger values (pFDR<.10 to pFDR<. 001). A repeated measures ANOVA design with the factor condition (1×7) revealed no significant differences for the number of trials submitted between conditions (F = .92, p = .48). The initial permutation analysis (1×7) revealed significant ERSPs in the 15–20 Hz range (beta) the left µ component and the 15–25 Hz range for the right hemisphere component corrected across the entire time-frequency matrix (pFDR<.05; 69×105) (see Figure 5). Significant time-frequency values were found in the time-periods prior to, during, and after stimulus onset with a peak event-related decreases in spectral power (i.e., ERD) in the time period after stimulus offset. To determine the sources of condition effects, two separate ANOVA designs were computed using the STUDY command structure. Because the time periods before, during, and after stimulus onset were of interest, all subsequent analysis were based upon the time period from −600 ms to 1200 ms. In other words, as the total time to present a stimulus was 600 ms, all times and frequencies between 3 and 40 Hz for time periods 600 ms prior to until 1200 ms following the stimulus were investigated to test proposed hypotheses.


Suppression of the µ rhythm during speech and non-speech discrimination revealed by independent component analysis: implications for sensorimotor integration in speech processing.

Bowers A, Saltuklaroglu T, Harkrider A, Cuellar M - PLoS ONE (2013)

Mean left and right hemisphere µ time-frequency ERSPs (event-related spectral perturbations).ERSPs are scaled in the same root-mean-square decibel units as a function of condition (1×7) and random effects analysis indicating significant values in the traditional beta (13–30 Hz) and alpha ranges (8–13 Hz). Non-significant values are colored green, with significant values shown in orange and red. Event-related decreases in spectral power are indicated in blue (−4.5) and increases are indicated in red (4.5).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3750026&req=5

pone-0072024-g005: Mean left and right hemisphere µ time-frequency ERSPs (event-related spectral perturbations).ERSPs are scaled in the same root-mean-square decibel units as a function of condition (1×7) and random effects analysis indicating significant values in the traditional beta (13–30 Hz) and alpha ranges (8–13 Hz). Non-significant values are colored green, with significant values shown in orange and red. Event-related decreases in spectral power are indicated in blue (−4.5) and increases are indicated in red (4.5).
Mentions: Left and Right μ mean ERSP values across subjects and conditions are shown in a time-frequency map with corrected significance values for condition in a separate map (Figure 5). Non-significant values are depicted in green and significant values are depicted in color from orange for weaker values to red for stronger values (pFDR<.10 to pFDR<. 001). A repeated measures ANOVA design with the factor condition (1×7) revealed no significant differences for the number of trials submitted between conditions (F = .92, p = .48). The initial permutation analysis (1×7) revealed significant ERSPs in the 15–20 Hz range (beta) the left µ component and the 15–25 Hz range for the right hemisphere component corrected across the entire time-frequency matrix (pFDR<.05; 69×105) (see Figure 5). Significant time-frequency values were found in the time-periods prior to, during, and after stimulus onset with a peak event-related decreases in spectral power (i.e., ERD) in the time period after stimulus offset. To determine the sources of condition effects, two separate ANOVA designs were computed using the STUDY command structure. Because the time periods before, during, and after stimulus onset were of interest, all subsequent analysis were based upon the time period from −600 ms to 1200 ms. In other words, as the total time to present a stimulus was 600 ms, all times and frequencies between 3 and 40 Hz for time periods 600 ms prior to until 1200 ms following the stimulus were investigated to test proposed hypotheses.

Bottom Line: EEG data were decomposed using independent component analysis and clustered across participants using principle component methods in EEGLAB.ICA 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.Future directions and possible translational value for clinical populations in which sensorimotor integration may play a functional role are discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Communication Disorders, University of Arkansas, Fayetteville, Arkansas, United States of America.

ABSTRACT

Background: Constructivist 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.).

Methods: Sixteen 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.

Results: ICA 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.

Conclusions: Findings 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.

Show MeSH
Related in: MedlinePlus