Spoken language exists because of a remarkable neural process. Inside a speaker's brain, an intended message gives rise to neural signals activating the muscles of the vocal tract. The process is ...remarkable because these muscles are activated in just the right way that the vocal tract produces sounds a listener understands as the intended message. What is the best approach to understanding the neural substrate of this crucial motor control process? One of the key recent modeling developments in neuroscience has been the use of state feedback control (SFC) theory to explain the role of the CNS in motor control. SFC postulates that the CNS controls motor output by (1) estimating the current dynamic state of the thing (e.g., arm) being controlled, and (2) generating controls based on this estimated state. SFC has successfully predicted a great range of non-speech motor phenomena, but as yet has not received attention in the speech motor control community. Here, we review some of the key characteristics of speech motor control and what they say about the role of the CNS in the process. We then discuss prior efforts to model the role of CNS in speech motor control, and argue that these models have inherent limitations - limitations that are overcome by an SFC model of speech motor control which we describe. We conclude by discussing a plausible neural substrate of our model.
How precisely does the brain predict the sensory consequences of our actions? Efference copy is thought to reflect the predicted sensation of self-produced motor acts, such as the auditory feedback ...heard while speaking. Here, we use magnetoencephalographic imaging (MEG-I) in human speakers to demonstrate that efference copy prediction does not track movement variability across repetitions of the same motor task. Specifically, spoken vowels were less accurately predicted when they were less similar to a speaker's median production, even though the prediction is thought to be based on the very motor commands that generate each vowel. Auditory cortical responses to less prototypical speech productions were less suppressed, resembling responses to speech errors, and were correlated with later corrective movement, suggesting that the suppression may be functionally significant for error correction. The failure of the motor system to accurately predict less prototypical speech productions suggests that the efferent-driven suppression does not reflect a sensory prediction, but a sensory goal.
The control of vocalization is critically dependent on auditory feedback. Here, we determined the human peri-Sylvian speech network that mediates feedback control of pitch using direct cortical ...recordings. Subjects phonated while a real-time signal processor briefly perturbed their output pitch (speak condition). Subjects later heard the same recordings of their auditory feedback (listen condition). In posterior superior temporal gyrus, a proportion of sites had suppressed responses to normal feedback, whereas other spatially independent sites had enhanced responses to altered feedback. Behaviorally, speakers compensated for perturbations by changing their pitch. Single-trial analyses revealed that compensatory vocal changes were predicted by the magnitude of both auditory and subsequent ventral premotor responses to perturbations. Furthermore, sites whose responses to perturbation were enhanced in the speaking condition exhibited stronger correlations with behavior. This sensorimotor cortical network appears to underlie auditory feedback-based control of vocal pitch in humans.
Highlights • State feedback control (SFC) model is described for speech motor control. • SFC explains auditory suppression/enhancement depending on motor predictions. • SFC explains auditory-driven ...vocal compensation during feedback pertubation.
Auditory feedback is used to monitor and correct for errors in speech production, and one of the clearest demonstrations of this is the pitch perturbation reflex. During ongoing phonation, speakers ...respond rapidly to shifts of the pitch of their auditory feedback, altering their pitch production to oppose the direction of the applied pitch shift. In this study, we examine the timing of activity within a network of brain regions thought to be involved in mediating this behavior. To isolate auditory feedback processing relevant for motor control of speech, we used magnetoencephalography (MEG) to compare neural responses to speech onset and to transient (400ms) pitch feedback perturbations during speaking with responses to identical acoustic stimuli during passive listening. We found overlapping, but distinct bilateral cortical networks involved in monitoring speech onset and feedback alterations in ongoing speech. Responses to speech onset during speaking were suppressed in bilateral auditory and left ventral supramarginal gyrus/posterior superior temporal sulcus (vSMG/pSTS). In contrast, during pitch perturbations, activity was enhanced in bilateral vSMG/pSTS, bilateral premotor cortex, right primary auditory cortex, and left higher order auditory cortex. We also found speaking-induced delays in responses to both unaltered and altered speech in bilateral primary and secondary auditory regions, left vSMG/pSTS and right premotor cortex. The network dynamics reveal the cortical processing involved in both detecting the speech error and updating the motor plan to create the new pitch output. These results implicate vSMG/pSTS as critical in both monitoring auditory feedback and initiating rapid compensation to feedback errors.
•Distinct bilateral cortical networks monitor speech onset and feedback alterations.•Speaking onset induces suppression and delays in the auditory cortices.•Auditory, motor cortices have enhanced and delayed responses to pitch perturbations.•Parieto-temporal junction is critical in feedback monitoring and compensation.
Sensory responses to stimuli that are triggered by a self-initiated motor act are suppressed when compared with the response to the same stimuli triggered externally, a phenomenon referred to as ...motor-induced suppression (MIS) of sensory cortical feedback. Studies in the somatosensory system suggest that such suppression might be sensitive to delays between the motor act and the stimulus onset, and a recent study in the auditory system suggests that such MIS develops rapidly. In three MEG experiments, we characterize the properties of MIS by examining the M100 response from the auditory cortex to a simple tone triggered by a button press. In Experiment 1, we found that MIS develops for zero delays but does not generalize to nonzero delays. In Experiment 2, we found that MIS developed for 100-msec delays within 300 trials and occurs in excess of auditory habituation. In Experiment 3, we found that unlike MIS for zero delays, MIS for nonzero delays does not exhibit sensitivity to sensory, delay, or motor-command changes. These results are discussed in relation to suppression to self-produced speech and a general model of sensory motor processing and control.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Lesion-based mapping of speech pathways has been possible only during invasive neurosurgical procedures using direct cortical stimulation (DCS). However, navigated transcranial magnetic stimulation ...(nTMS) may allow for lesion-based interrogation of language pathways noninvasively. Although not lesion-based, magnetoencephalographic imaging (MEGI) is another noninvasive modality for language mapping. In this study, we compare the accuracy of nTMS and MEGI with DCS.
Subjects with lesions around cortical language areas underwent preoperative nTMS and MEGI for language mapping. nTMS maps were generated using a repetitive TMS protocol to deliver trains of stimulations during a picture naming task. MEGI activation maps were derived from adaptive spatial filtering of beta-band power decreases prior to overt speech during picture naming and verb generation tasks. The subjects subsequently underwent awake language mapping via intraoperative DCS. The language maps obtained from each of the 3 modalities were recorded and compared.
nTMS and MEGI were performed on 12 subjects. nTMS yielded 21 positive language disruption sites (11 speech arrest, 5 anomia, and 5 other) while DCS yielded 10 positive sites (2 speech arrest, 5 anomia, and 3 other). MEGI isolated 32 sites of peak activation with language tasks. Positive language sites were most commonly found in the pars opercularis for all three modalities. In 9 instances the positive DCS site corresponded to a positive nTMS site, while in 1 instance it did not. In 4 instances, a positive nTMS site corresponded to a negative DCS site, while 169 instances of negative nTMS and DCS were recorded. The sensitivity of nTMS was therefore 90%, specificity was 98%, the positive predictive value was 69% and the negative predictive value was 99% as compared with intraoperative DCS. MEGI language sites for verb generation and object naming correlated with nTMS sites in 5 subjects, and with DCS sites in 2 subjects.
Maps of language function generated with nTMS correlate well with those generated by DCS. Negative nTMS mapping also correlates with negative DCS mapping. In our study, MEGI lacks the same level of correlation with intraoperative mapping; nevertheless it provides useful adjunct information in some cases. nTMS may offer a lesion-based method for noninvasively interrogating language pathways and be valuable in managing patients with peri-eloquent lesions.
•Navigated TMS is a safe, noninvasive method for lesion-based mapping of language pathways.•nTMS is safe, well-tolerated by patients, and can be performed in a lab environment.•nTMS-based language maps correlate well with maps from direct cortical stimulation.•nTMS maps are less well correlated with maps from magnetoencephalographic imaging.•nTMS is useful for interrogating language pathways for research and clinical purposes.
Control of speech formants is important for the production of distinguishable speech sounds and is achieved with both feedback and learned feedforward control. However, it is unclear whether the ...learning of feedforward control involves the mechanisms of feedback control. Speakers have been shown to compensate for unpredictable transient mid-utterance perturbations of pitch and loudness feedback, demonstrating online feedback control of these speech features. To determine whether similar feedback control mechanisms exist in the production of formants, responses to unpredictable vowel formant feedback perturbations were examined. Results showed similar within-trial compensatory responses to formant perturbations that were presented at utterance onset and mid-utterance. The relationship between online feedback compensation to unpredictable formant perturbations and sensorimotor adaptation to consistent formant perturbations was further examined. Within-trial online compensation responses were not correlated with across-trial sensorimotor adaptation. A detailed analysis of within-trial time course dynamics across trials during sensorimotor adaptation revealed that across-trial sensorimotor adaptation responses did not result from an incorporation of within-trial compensation response. These findings suggest that online feedback compensation and sensorimotor adaptation are governed by distinct neural mechanisms. These findings have important implications for models of speech motor control in terms of how feedback and feedforward control mechanisms are implemented.
As we talk, we unconsciously adjust our speech to ensure it sounds the way we intend it to sound. However, because speech production involves complex motor planning and execution, no two utterances ...of the same sound will be exactly the same. Here, we show that auditory cortex is sensitive to natural variations in self-produced speech from utterance to utterance. We recorded event-related potentials (ERPs) from ninety-nine subjects while they uttered "ah" and while they listened to those speech sounds played back. Subjects' utterances were sorted based on their formant deviations from the previous utterance. Typically, the N1 ERP component is suppressed during talking compared to listening. By comparing ERPs to the least and most variable utterances, we found that N1 was less suppressed to utterances that differed greatly from their preceding neighbors. In contrast, an utterance's difference from the median formant values did not affect N1. Trial-to-trial pitch (f0) deviation and pitch difference from the median similarly did not affect N1. We discuss mechanisms that may underlie the change in N1 suppression resulting from trial-to-trial formant change. Deviant utterances require additional auditory cortical processing, suggesting that speaking-induced suppression mechanisms are optimally tuned for a specific production.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Several behavioral and brain imaging studies have demonstrated a significant interaction between speech perception and speech production. In this study, auditory cortical responses to speech were ...examined during self-production and feedback alteration. Magnetic field recordings were obtained from both hemispheres in subjects who spoke while hearing controlled acoustic versions of their speech feedback via earphones. These responses were compared to recordings made while subjects listened to a tape playback of their production. The amplitude of tape playback was adjusted to match the amplitude of self-produced speech. Recordings of evoked responses to both self-produced and tape-recorded speech were obtained free of movement-related artifacts. Responses to self-produced speech were weaker than were responses to tape-recorded speech. Responses to tones were also weaker during speech production, when compared with responses to tones recorded in the presence of speech from tape playback. However, responses evoked by gated noise stimuli did not differ for recordings made during self-produced speech versus recordings made during tape-recorded speech playback. These data suggest that during speech production, the auditory cortex (1) attenuates its sensitivity and (2) modulates its activity as a function of the expected acoustic feedback.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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