Somatosensory Feedback Control Research Articles

Modeling Responses to Auditory Feedback Perturbations in Adults, Children, and Children With Complex Speech Sound Disorders: Evidence for Impaired Auditory Self-Monitoring?

Previous studies have found that typically developing (TD) children were able to compensate and adapt to auditory feedback perturbations to a similar or larger degree compared to young adults, while children with speech sound disorder (SSD) were found to produce predominantly following responses. However, large individual differences lie underneath the group-level results. This study investigates possible mechanisms in responses to formant shifts by modeling parameters of feedback and feedforward control of speech production based on behavioral data. SimpleDIVA was used to model an existing dataset of compensation/adaptation behavior to auditory feedback perturbations collected from three groups of Dutch speakers: 50 young adults, twenty-three 4- to 8-year-old children with TD speech, and seven 4- to 8-year-old children with SSD. Between-groups and individual within-group differences in model outcome measures representing auditory and somatosensory feedback control gain and feedforward learning rate were assessed. Notable between-groups and within-group variation was found for all outcome measures. Data modeled for individual speakers yielded model fits with varying reliability. Auditory feedback control gain was negative in children with SSD and positive in both other groups. Somatosensory feedback control gain was negative for both groups of children and marginally negative for adults. Feedforward learning rate measures were highest in the children with TD speech followed by children with SSD, compared to adults. The SimpleDIVA model was able to account for responses to the perturbation of auditory feedback other than corrective, as negative auditory feedback control gains were associated with following responses to vowel shifts. These preliminary findings are suggestive of impaired auditory self-monitoring in children with complex SSD. Possible mechanisms underlying the nature of following responses are discussed.

Subthreshold electrical stimulation with pink noise enhances feedback control as evaluated by scaling exponent of postural sway

Subthreshold somatosensory stimulation with pink noise has been shown to attenuate postural sway better than stimulation with white noise. This might be due to the different frequency structures of the noise signals. However, their effects on the underlying somatosensory feedback pathway are still unknown. Thus, we aimed to determine whether pink noise enhances the somatosensory feedback pathway more effectively than other noises with different frequency structures, such as white and red noises. Sixteen young adults stood quietly for 65 s under four stimulation conditions: no stimulation and stimulations with white-, pink-, and red-noise-like signals. Based on a stabilogram-diffusion analysis, we calculated the long-term diffusion coefficient and scaling exponent in the radial direction to evaluate the effects of these noise signals on their somatosensory feedback control. The root mean square (CoPRMS) and mean velocity of the foot center of pressure were also computed to assess the amount of postural sway. The results showed that the stimulation condition had a significant effect on the scaling exponent, with the value under the pink-noise-like signal significantly lower than that under the no-stimulation condition. We also found that among the participants, the percentage of reduction in CoPRMS by the pink-noise-like signal was positively correlated with the CoPRMS value under the no-stimulation condition. Altogether, the somatosensory feedback control for balancing for quiet standing posture was improved by pink noise, and its effect on the variability of postural sway correlated with inherent postural sway variability.

Auditory and somatosensory feedback mechanisms of laryngeal and articulatory speech motor control.

Speech production is a complex motor task involving multiple subsystems. The relationships between these subsystems need to be comprehensively investigated to understand the underlying mechanisms of speech production. The goal of this paper is to examine the differential contributions of 1) auditory and somatosensory feedback control mechanisms, and 2) laryngeal and articulatory speech production subsystems on speech motor control at an individual speaker level using altered auditory and somatosensory feedback paradigms. Twenty young adults completed speaking tasks in which sudden and unpredictable auditory and physical perturbations were applied to the laryngeal and articulatory speech production subsystems. Auditory perturbations were applied to laryngeal or articulatory acoustic features of speech. Physical perturbations were applied to the larynx and the jaw. Pearson-product moment correlation coefficients were calculated between 1) auditory and somatosensory reflexive responses to investigate relationships between auditory and somatosensory feedback control mechanisms, and 2) laryngeal and articulatory reflexive responses as well as acuity measures to investigate the relationship between auditory-motor features of laryngeal and articulatory subsystems. No statistically significant correlations were found concerning the relationships between auditory and somatosensory feedback. No statistically significant correlations were found between auditory-motor features in the laryngeal and articulatory control subsystems. Results suggest that the laryngeal and articulatory speech production subsystems operate with differential auditory and somatosensory feedback control mechanisms. The outcomes suggest that current models of speech motor control should consider decoupling laryngeal and articulatory domains to better model speech motor control processes.

Auditory Feedback Control Mechanisms Do Not Contribute to Cortical Hyperactivity Within the Voice Production Network in Adductor Spasmodic Dysphonia.

Purpose Adductor spasmodic dysphonia (ADSD), the most common form of spasmodic dysphonia, is a debilitating voice disorder characterized by hyperactivity and muscle spasms in the vocal folds during speech. Prior neuroimaging studies have noted excessive brain activity during speech in participants with ADSD compared to controls. Speech involves an auditory feedback control mechanism that generates motor commands aimed at eliminating disparities between desired and actual auditory signals. Thus, excessive neural activity in ADSD during speech may reflect, at least in part, increased engagement of the auditory feedback control mechanism as it attempts to correct vocal production errors detected through audition. Method To test this possibility, functional magnetic resonance imaging was used to identify differences between participants with ADSD (n = 12) and age-matched controls (n = 12) in (a) brain activity when producing speech under different auditory feedback conditions and (b) resting-state functional connectivity within the cortical network responsible for vocalization. Results As seen in prior studies, the ADSD group had significantly higher activity than the control group during speech with normal auditory feedback (compared to a silent baseline task) in three left-hemisphere cortical regions: ventral Rolandic (sensorimotor) cortex, anterior planum temporale, and posterior superior temporal gyrus/planum temporale. Importantly, this same pattern of hyperactivity was also found when auditory feedback control of speech was eliminated through masking noise. Furthermore, the ADSD group had significantly higher resting-state functional connectivity between sensorimotor and auditory cortical regions within the left hemisphere as well as between the left and right hemispheres. Conclusions Together, our results indicate that hyperactivation in the cortical speech network of individuals with ADSD does not result from hyperactive auditory feedback control mechanisms and rather is likely related to impairments in somatosensory feedback control and/or feedforward control mechanisms.

A Simple 3-Parameter Model for Examining Adaptation in Speech and Voice Production.

Sensorimotor adaptation experiments are commonly used to examine motor learning behavior and to uncover information about the underlying control mechanisms of many motor behaviors, including speech production. In the speech and voice domains, aspects of the acoustic signal are shifted/perturbed over time via auditory feedback manipulations. In response, speakers alter their production in the opposite direction of the shift so that their perceived production is closer to what they intended. This process relies on a combination of feedback and feedforward control mechanisms that are difficult to disentangle. The current study describes and tests a simple 3-parameter mathematical model that quantifies the relative contribution of feedback and feedforward control mechanisms to sensorimotor adaptation. The model is a simplified version of the DIVA model, an adaptive neural network model of speech motor control. The three fitting parameters of SimpleDIVA are associated with the three key subsystems involved in speech motor control, namely auditory feedback control, somatosensory feedback control, and feedforward control. The model is tested through computer simulations that identify optimal model fits to six existing sensorimotor adaptation datasets. We show its utility in (1) interpreting the results of adaptation experiments involving the first and second formant frequencies as well as fundamental frequency; (2) assessing the effects of masking noise in adaptation paradigms; (3) fitting more than one perturbation dimension simultaneously; (4) examining sensorimotor adaptation at different timepoints in the production signal; and (5) quantitatively predicting responses in one experiment using parameters derived from another experiment. The model simulations produce excellent fits to real data across different types of perturbations and experimental paradigms (mean correlation between data and model fits across all six studies = 0.95 ± 0.02). The model parameters provide a mechanistic explanation for the behavioral responses to the adaptation paradigm that are not readily available from the behavioral responses alone. Overall, SimpleDIVA offers new insights into speech and voice motor control and has the potential to inform future directions of speech rehabilitation research in disordered populations. Simulation software, including an easy-to-use graphical user interface, is publicly available to facilitate the use of the model in future studies.

Changes in brain activity following intensive voice treatment in children with cerebral palsy.

Eight children (3 females; 8-16 years) with motor speech disorders secondary to cerebral palsy underwent 4weeks of an intensive neuroplasticity-principled voice treatment protocol, LSVT LOUD® , followed by a structured 12-week maintenance program. Children were asked to overtly produce phonation (ah) at conversational loudness, cued-phonation at perceived twice-conversational loudness, a series of single words, and a prosodic imitation task while being scanned using fMRI, immediately pre- and post-treatment and 12 weeks following a maintenance program. Eight age- and sex-matched controls were scanned at each of the same three time points. Based on the speech and language literature, 16 bilateral regions of interest were selected a priori to detect potential neural changes following treatment. Reduced neural activity in the motor areas (decreased motor system effort) before and immediately after treatment, and increased activity in the anterior cingulate gyrus after treatment (increased contribution of decision making processes) were observed in the group with cerebral palsy compared to the control group. Using graphical models, post-treatment changes in connectivity were observed between the left supramarginal gyrus and the right supramarginal gyrus and the left precentral gyrus for the children with cerebral palsy, suggesting LSVT LOUD enhanced contributions of the feedback system in the speech production network instead of high reliance on feedforward control system and the somatosensory target map for regulating vocal effort. Network pruning indicates greater processing efficiency and the recruitment of the auditory and somatosensory feedback control systems following intensive treatment. Hum Brain Mapp 38:4413-4429, 2017. © 2017 Wiley Periodicals, Inc.

Neuroimaging of the speech network

Historically, the study of the neural underpinnings of speech has suffered from the lack of an animal model whose brain activity could be measured using invasive electrophysiological techniques. The development of non-invasive structural and functional neuroimaging techniques in the latter part of the 20th century has led to a dramatic improvement in our understanding of the network of brain regions responsible for speech production. Techniques for measuring regional cerebral blood flow, including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), have illuminated the neural regions involved in various aspects of speech, including feedforward control mechanisms as well as auditory and somatosensory feedback control circuits. More recently, fMRI studies utilizing repetition suppression have been used to identify the neural representations used in different parts of the speech network, including the identification of a syllable representation in left ventral premotor cortex. Magnetic resonance imaging has also been used to investigate the anatomical structure of the speech network, providing crucial information regarding connectivity within the network as well as identifying anomalies in the sizes of neural regions and/or white matter pathways in speech disorders.

Sensory feedback control in speech: Neural circuits and individual differences

Speech production involves a combination of feedforward and sensory feedback-based control mechanisms. The latter have been characterized in experiments involving real-time perturbations during speech. Unexpected perturbations of auditory feedback result in corrective motor responses with a minimum latency of approximately 100 ms after perturbation onset. These responses have been shown for both pitch and formant frequency perturbations, and the responsible neural circuitry includes portions of the superior temporal gyrus and ventral premotor cortex (vPMC). Corrective responses to somatosensory perturbations (such as a downward force applied to the jaw) occur approximately 50 ms after perturbation onset and are mediated by ventral somatosensory cortex and vPMC. The degree to which an individual weights auditory versus somatosensory feedback varies substantially. Such differences rely in part on differences in sensory acuity, e.g., a speaker with relatively poor hearing is likely to rely more heavily on somatosensory feedback control mechanisms than auditory feedback control mechanisms. Additionally, reliance on feedback control may be modulated to compensate for impairments in the feedforward system for speech, e.g., adults who stutter show a reduced reliance on auditory feedback control compared to fluent speakers, perhaps because auditory feedback can have a deleterious effect on speech initiation in stuttering.

Auditory feedback control is involved at even sub-phonemic levels of speech production

In the target article, Hickok asserts that auditory and somatosensory feedback control operate at different levels in the speech production hierarchy, with somatosensory feedback control involved in lower-level phonemic processing and auditory feedback control involved in higher-level syllabic processing. This assertion is based in part on a characterization of phonemes as timeless feature bundles. In this commentary I argue that the linguistic conception of phonemes as timeless feature bundles is insufficient for characterizing the production of phonemes by the motor system, and I review evidence that auditory feedback control is used even at the sub-phonemic level of speech production, contrary to its role in the hierarchical state feedback control model.

Functional neuroanatomy of vocalization in patients with Parkinson's disease

In Parkinson's disease (PD) both speech production and self-monitoring of voiced speech are altered. Methods In our previous study we used functional magnetic resonance imaging (fMRI) to examine which brain areas are involved in overt reading in nine female PD patients (mean age 66.0 ± 11.6 years) compared with eight age-matched healthy female controls (mean age 62.2 years ± 12.3). Here we performed the post-hoc seed-based functional connectivity analysis of our data to assess the functional connectivity between the periaqueductal gray matter (PAG; i.e. the core subcortical structure involved in human vocalization) and other brain regions in the same groups of PD patients and controls. Results In PD patients as compared with controls we observed increased connectivity between PAG and basal ganglia, posterior superior temporal gyrus, supramarginal and fusiform gyri and inferior parietal lobule on the right side. In the PD group, the connectivity strength in the right putamen and the right sypramarginal gyrus was correlated with variability of pitch while the connectivity strength in the right posterior superior temporal gyrus and in the right inferior parietal lobule was correlated with speech loudness. Conclusion We observed functional reorganization in PD patients as compared with controls in both the motor basal ganglia-thalamo-cortical circuitry and cortical areas known to be engaged in-auditory and somatosensory feedback control of voiced speech. These changes were hemisphere-specific and might either reflect effects of dopaminergic treatment or at least partially successful compensatory mechanisms involved in early-stage PD.

FMRI investigation of unexpected somatosensory feedback perturbation during speech

Somatosensory feedback plays a critical role in the coordination of articulator movements for speech production. In response to unexpected resistance to lip or jaw movements during speech, fluent speakers can use the difference between the somatosensory expectations of a speech sound and the actual somatosensory feedback to adjust the trajectories of functionally relevant but unimpeded articulators. In an effort to investigate the neural substrates underlying the somatosensory feedback control of speech, we used an event-related sparse sampling functional magnetic resonance imaging paradigm and a novel pneumatic device that unpredictably blocked subjects' jaw movements. In comparison to speech, perturbed speech, in which jaw perturbation prompted the generation of compensatory speech motor commands, demonstrated increased effects in bilateral ventral motor cortex, right-lateralized anterior supramarginal gyrus, inferior frontal gyrus pars triangularis and ventral premotor cortex, and bilateral inferior posterior cerebellum (lobule VIII). Structural equation modeling revealed a significant increased influence from left anterior supramarginal gyrus to right anterior supramarginal gyrus and from left anterior supramarginal gyrus to right ventral premotor cortex as well as a significant increased reciprocal influence between right ventral premotor cortex and right ventral motor cortex and right anterior supramarginal gyrus and right inferior frontal gyrus pars triangularis for perturbed speech relative to speech. These results suggest that bilateral anterior supramarginal gyrus, right inferior frontal gyrus pars triangularis, right ventral premotor and motor cortices are functionally coupled and influence speech motor output when somatosensory feedback is unexpectedly perturbed during speech production.

Cortical interactions underlying the production of speech sounds

Speech production involves the integration of auditory, somatosensory, and motor information in the brain. This article describes a model of speech motor control in which a feedforward control system, involving premotor and primary motor cortex and the cerebellum, works in concert with auditory and somatosensory feedback control systems that involve both sensory and motor cortical areas. New speech sounds are learned by first storing an auditory target for the sound, then using the auditory feedback control system to control production of the sound in early repetitions. Repeated production of the sound leads to tuning of feedforward commands which eventually supplant the feedback-based control signals. Although parts of the model remain speculative, it accounts for a wide range of kinematic, acoustic, and neuroimaging data collected during speech production and provides a framework for investigating communication disorders that involve malfunction of the cerebral cortex and interconnected subcortical structures. Learning outcomes: Readers will be able to: (1) describe several types of learning that occur in the sensory–motor system during babbling and early speech, (2) identify three neural control subsystems involved in speech production, (3) identify regions of the brain involved in monitoring auditory and somatosensory feedback during speech production, and (4) identify regions of the brain involved in feedforward control of speech.

Limits to Fast-Conducting Somatosensory Feedback in Movement Control

Somatosensory feedback occurs during every movement performed. The transmission of this feedback is dynamically regulated during movement. This review addresses the limits of somatosensory feedback control of human movement and proposes that dynamic modification of feedback inflow requires an increased reliance on internal models for movement control.