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. 2015 Oct 21;35(42):14183-94.
doi: 10.1523/JNEUROSCI.3610-14.2015.

Multifunctional and Context-Dependent Control of Vocal Acoustics by Individual Muscles

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Free PMC article

Multifunctional and Context-Dependent Control of Vocal Acoustics by Individual Muscles

Kyle H Srivastava et al. J Neurosci. .
Free PMC article

Abstract

The relationship between muscle activity and behavioral output determines how the brain controls and modifies complex skills. In vocal control, ensembles of muscles are used to precisely tune single acoustic parameters such as fundamental frequency and sound amplitude. If individual vocal muscles were dedicated to the control of single parameters, then the brain could control each parameter independently by modulating the appropriate muscle or muscles. Alternatively, if each muscle influenced multiple parameters, a more complex control strategy would be required to selectively modulate a single parameter. Additionally, it is unknown whether the function of single muscles is fixed or varies across different vocal gestures. A fixed relationship would allow the brain to use the same changes in muscle activation to, for example, increase the fundamental frequency of different vocal gestures, whereas a context-dependent scheme would require the brain to calculate different motor modifications in each case. We tested the hypothesis that single muscles control multiple acoustic parameters and that the function of single muscles varies across gestures using three complementary approaches. First, we recorded electromyographic data from vocal muscles in singing Bengalese finches. Second, we electrically perturbed the activity of single muscles during song. Third, we developed an ex vivo technique to analyze the biomechanical and acoustic consequences of single-muscle perturbations. We found that single muscles drive changes in multiple parameters and that the function of single muscles differs across vocal gestures, suggesting that the brain uses a complex, gesture-dependent control scheme to regulate vocal output.

Keywords: Bengalese finch; electromyography; motor control; muscle stimulation; vocal muscle.

Figures

Figure 1.
Figure 1.
Vocal muscle activity correlates with vocal acoustics in Bengalese finch song. a, Birdsong is made up of vocal gestures called syllables, labeled A through F above the spectrogram, which show the power at different acoustic frequencies as a function of time. The vertical red line represents the time at which acoustic parameters were measured during syllable C (see Materials and Methods). b, To measure EMG activity and electrically stimulate single muscles, pairs of electrodes were inserted in individual vocal muscles (DTB and VS). VTB and LDS, which were used as reference muscles in the ex vivo experiments, are also shown. Anatomy illustration modified and used with permission from Düring et al. (2013). Muscles are shown as colored structures while the trachea (top) and bronchi (bottom) are shown in white. c, d, The raw EMG signal (c) and smoothed, rectified EMG signal (d) from the EXP. Red box shows the time window over which EMG activity was quantified just before acoustic measurement time. e, A linear regression was then calculated between the smoothed, rectified EMG and each acoustic parameter (sound amplitude is shown). AUs, Arbitrary units.
Figure 2.
Figure 2.
Measuring the latency of muscle stimulation effects. a, The timing of stimulation was varied relative to the time of fundamental frequency (FF) quantification (green dashed line). The red traces schematize six different stimulation times relative to the syllable. b, Across all experiments, the minimum latency for detected acoustic effects was 5 ms. Acoustic effects measured later than this latency were similar in direction but could be larger in magnitude. In all stimulation analysis, acoustic effects were measured between 5 ms and up to 30 ms after stimulation onset, depending on the number of data points available (see Materials and Methods).
Figure 3.
Figure 3.
Image analysis of ex vivo muscle perturbation suggests stimulation currents used in the in vivo experiments specifically shortened targeted muscle. a, Schematic of experimental setup (see Materials and Methods). b, Markers were selected on the first image of an ex vivo stimulation run. c, The distances between selected markers were tracked over the course of the video. Strain (change on y-axis label) is shown for a stimulation current of 400 μA for syringeal muscles VS (red) and VTB (blue). d, Percentage change in length during stimulation is plotted against the magnitude of the stimulating current. Data points from example in c are shown as squares. While VS (red) showed significant contraction at low currents (t(16) = 7.8, p < 0.01, two-tailed, paired t test), VTB (blue) did not begin to significantly contract until the current size reaches 600 μA (t(16) = 0.51, p = 0.62, two-tailed, paired t test).
Figure 4.
Figure 4.
EMG activity had significant regressions with multiple acoustic parameters across vocal muscles. Significant linear regressions were found between EMG activity and multiple acoustic parameters in all muscles recorded. Across all muscle–syllable pairs (N = 112), 78% featured significant regression slopes with at least one acoustic parameter. Fifty-three percent of those (41% overall) exhibited significant regressions with multiple acoustic parameters. FF, fundamental frequency.
Figure 5.
Figure 5.
Targeted vocal muscle activation drives short-latency acoustic effects. a, In a catch trial, on-line acoustic analysis (see Materials and Methods) detected the initial part of the syllable (red line), but no stimulation occurred. b, In a stimulated trial (using a 500 μA current), the muscle is perturbed 20 ms after detection (first red line indicates detection time while the second red line indicates the onset of stimulation). c, The difference in spectrograms shows an increase in fundamental frequency. All plots show the mean spectrogram over all trials.
Figure 6.
Figure 6.
Targeted muscle stimulation drives significant changes in multiple acoustic parameters in singing birds. In the syllable and muscle shown in Figure 5b, a stimulating (Stim) current of 75 μA caused fundamental frequency (FF) to significantly increase (a; t(230) = 3.5, p < 0.01, two-tailed, two-sample t test), sound amplitude to significantly increase (b; t(230) = 3.9, p < 0.01, two-tailed, two-sample t test), and spectral entropy to significantly decrease (c; t(230) = 8.8, p < 0.01, two-tailed, two-sample t test). d, Perturbation of VS altered multiple acoustic parameters in 80% of cases (N = 5), while perturbation of DTB did so in 100% of cases (N = 6). *p < 0.05.
Figure 7.
Figure 7.
Ex vivo activation of VS demonstrates changes in multiple acoustic parameters. a, Sound generated by an ex vivo syrinx displayed an upward shift in fundamental frequency (FF) when stimulation is delivered within VS. b, Across all ex vivo trials activating VS, there was a significant increase in fundamental frequency and a significant decrease in amplitude, both of which varied with current size (p < 0.001, linear regression). The trial depicted in a was marked by an enlarged, green square for each parameter.
Figure 8.
Figure 8.
The sign of the correlation between single vocal muscles and acoustic parameters can vary depending on the syllable. a, EMG activity in EXP was recorded during song (plotting conventions as in Fig. 1a–c). b, In syllable A, the linear regression between EMG activity and fundamental frequency (FF) had a positive slope, while in syllable D, the regression had a negative slope (p < 0.001, linear regression).
Figure 9.
Figure 9.
Vocal muscle activation drives opposite acoustic effects during different syllables. a, In one syllable, VS stimulation (500 μA) caused a significant decrease in amplitude (t(1537) = 16.2, p < 0.01, two-tailed, two-sample t test), while perturbation (500 μA) during another syllable caused a significant increase in amplitude (b; t(511) = 2.8, p < 0.01, two-tailed, two-sample t test). Their respective sliding RMS amplitudes showed a visible decrease (c) and increase (d) in amplitude. Dashed lines indicate mean amplitude trace ± 1 SE. Orange lines indicate where sound amplitudes were quantified for c and d. e, In a third syllable, DTB perturbation (500 μA) caused a significant decrease in fundamental frequency (FF; t(108) = 3.5, p < 0.01, two-tailed, two-sample t test), while perturbation (500 μA) during another syllable caused a significant increase in fundamental frequency (f; t(109) = 4.3, p < 0.01, two-tailed, two-sample t test). Their respective spectrograms showed the difference after stimulation (Stim; red bands in Stim-Catch spectrograms) with the first syllable (g) showing a decrease in fundamental frequency, and the second syllable (h) showing an increase in fundamental frequency. Green lines indicate where fundamental frequencies were quantified for e and f. Note that each syllable was from a different bird. *p < 0.05.
Figure 10.
Figure 10.
Vocal muscle perturbation effects are fixed for some acoustic parameters and are context dependent for others. a, Across all VS perturbations, fundamental frequency (FF) always increased with stimulation (p = 0.031, binomial test, N = 5), but amplitude and spectral entropy effects varied by syllable. b, Across all DTB perturbations, amplitude always decreased with stimulation (p = 0.016, binomial test, N = 6), while fundamental frequency and spectral entropy effects varied by syllable. *p < 0.05.

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