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Stuttering is a neurodevelopmental disorder characterized by impaired production of coordinated articulatory movements needed for fluent speech. It is currently unknown whether these abnormal production characteristics reflect disruptions to brain mechanisms underlying the acquisition and/or execution of speech motor sequences. To dissociate learning and control processes, we used a motor sequence learning paradigm to examine the behavioral and neural correlates of learning to produce novel phoneme sequences in adults who stutter (AWS) and neurotypical controls. Participants intensively practiced producing pseudowords containing non-native consonant clusters (e.g., “GVAZF”) over two days. The behavioral results indicated that although the two experimental groups showed comparable learning trajectories, AWS performed significantly worse on the task prior to and after speech motor practice. Using functional magnetic resonance imaging (fMRI), the authors compared brain activity during articulation of the practiced words and a set of novel pseudowords (matched in phonetic complexity). FMRI analyses revealed no differences between AWS and controls in cortical or subcortical regions; both groups showed comparable increases in activation in left-lateralized brain areas implicated in phonological working memory and speech motor planning during production of the novel sequences compared to the practiced sequences. Moreover, activation in left-lateralized basal ganglia sites was negatively correlated with in-scanner mean disfluency in AWS. Collectively, these findings demonstrate that AWS exhibit no deficit in constructing new speech motor sequences but do show impaired execution of these sequences before and after they have been acquired and consolidated.

BACKGROUND: The Directions Into Velocities of Articulators (DIVA) model and its partner, the Gradient Order DIVA (GODIVA) model, provide neurobiologically grounded, computational accounts of speech motor control and motor sequencing, with applications for the study and treatment of neurological motor speech disorders. AIMS: In this review, we provide an overview of the DIVA and GODIVA models and how they explain the interface between phonological and motor planning systems to build on previous models and provide a mechanistic accounting of apraxia of speech (AOS), a disorder of speech motor programming. MAIN CONTRIBUTION: Combined, the DIVA and GODIVA models account for both the segmental and suprasegmental features that define AOS via damage to (i) a speech sound map, hypothesized to reside in left ventral premotor cortex, (ii) a phonological content buffer hypothesized to reside in left posterior inferior frontal sulcus, and/or (iii) the axonal projections between these regions. This account is in line with a large body of behavioural work, and it unifies several prior theoretical accounts of AOS. CONCLUSIONS: The DIVA and GODIVA models provide an integrated framework for the generation and testing of both behavioural and neuroimaging hypotheses about the underlying neural mechanisms responsible for motor programming in typical speakers and in speakers with AOS.

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.

Vocal communication is a critical feature of social interaction across species; however, the relation between such behavior in humans and nonhumans remains unclear. To enable comparative investigation of this topic, we review the literature pertinent to interactive language use and identify the superset of cognitive operations involved in generating communicative action. We posit these functions comprise three intersecting multistep pathways: ( a) the Content Pathway, which selects the movements constituting a response; ( b) the Timing Pathway, which temporally structures responses; and ( c) the Affect Pathway, which modulates response parameters according to internal state. These processing streams form the basis of the Convergent Pathways for Interaction framework, which provides a conceptual model for investigating the cognitive and neural computations underlying vocal communication across species.

This study determined whether adults who stutter (AWS) exhibit deficits in responding to an auditory feedback timing perturbation, and whether external timing cues, which increase fluency, attenuate any disruptions due to altered temporal auditory feedback.Fifteen AWS and sixteen adults who do not stutter (ANS) read aloud a multisyllabic sentence either with normal pacing or with each syllable paced at the rate of a metronome. On random trials, an auditory feedback timing perturbation was applied, and timing responses were compared between groups and pacing conditions.Both groups responded to the timing perturbation by delaying subsequent syllable boundaries, and there were no significant differences between groups in either pacing condition. Furthermore, no response differences were found between normally paced and metronome-paced conditions.These findings are interpreted as showing that 1) AWS respond normally to pure timing perturbations, and 2) metronome-paced speech has no effect on online speech timing control as assessed in the present experiment.

Prior work has shown that Adults who stutter (AWS) have reduced and delayed responses to auditory feedback perturbations. This study aimed to determine whether external timing cues, which increase fluency, resolve auditory feedback processing disruptions.Fifteen AWS and sixteen adults who do not stutter (ANS) read aloud a multisyllabic sentence either with natural stress and timing or with each syllable paced at the rate of a metronome. On random trials, an auditory feedback formant perturbation was applied, and formant responses were compared between groups and pacing conditions.During normally paced speech, ANS showed a significant compensatory response to the perturbation by the end of the perturbed vowel, while AWS did not. In the metronome-paced condition, which significantly reduced the disfluency rate, the opposite was true: AWS showed a significant response by the end of the vowel, while ANS did not.These findings indicate a potential link between the reduction in stuttering found during metronome-paced speech and changes in auditory motor integration in AWS.

Many voice disorders are the result of intricate neural and/or biomechanical impairments that are poorly understood. The limited knowledge of their etiological and pathophysiological mechanisms hampers effective clinical management. Behavioral studies have been used concurrently with computational models to better understand typical and pathological laryngeal motor control. Thus far, however, a unified computational framework that quantitatively integrates physiologically relevant models of phonation with the neural control of speech has not been developed. Here, we introduce LaDIVA, a novel neurocomputational model with physiologically based laryngeal motor control. We combined the DIVA model (an established neural network model of speech motor control) with the extended body-cover model (a physics-based vocal fold model). The resulting integrated model, LaDIVA, was validated by comparing its model simulations with behavioral responses to perturbations of auditory vocal fundamental frequency (fo) feedback in adults with typical speech. LaDIVA demonstrated capability to simulate different modes of laryngeal motor control, ranging from short-term (i.e., reflexive) and long-term (i.e., adaptive) auditory feedback paradigms, to generating prosodic contours in speech. Simulations showed that LaDIVA’s laryngeal motor control displays properties of motor equivalence, i.e., LaDIVA could robustly generate compensatory responses to reflexive vocal fo perturbations with varying initial laryngeal muscle activation levels leading to the same output. The model can also generate prosodic contours for studying laryngeal motor control in running speech. LaDIVA can expand the understanding of the physiology of human phonation to enable, for the first time, the investigation of causal effects of neural motor control in the fine structure of the vocal signal.

Central nervous system (CNS) abnormalities and corresponding neurological and psychiatric symptoms are frequently observed in lysosomal storage disorders (LSDs). The genetic background of individual LSDs is indeed unique to each illness. However, resulting defective lysosomal function within the CNS can transition normal cellular processes (i.e., autophagy) into aberrant mechanisms, facilitating overlapping downstream consequences including neurocircuitry dysfunction, neurodegeneration as well as sensory, motor, cognitive, and psychological symptoms. Here, the neurological and biobehavioral phenotypes of major classes of LSDs are discussed alongside therapeutic strategies in development that aim to tackle neuropathology among other disease elements. Finally, focused ultrasound blood-brain barrier opening is proposed to enhance therapeutic delivery thereby overcoming the key hurdle of central distribution of disease modifying therapies in LSDs.

Purpose This study aimed to test whether (and how) somatosensory feedback signals from the vocal tract affect concurrent unimodal visual speech perception. Method Participants discriminated pairs of silent visual utterances of vowels under 3 experimental conditions: (a) normal (baseline) and while holding either (b) a bite block or (c) a lip tube in their mouths. To test the specificity of somatosensory–visual interactions during perception, we assessed discrimination of vowel contrasts optically distinguished based on their mandibular (English /ɛ/–/æ/) or labial (English /u/–French /u/) postures. In addition, we assessed perception of each contrast using dynamically articulating videos and static (single-frame) images of each gesture (at vowel midpoint). Results Engaging the jaw selectively facilitated perception of the dynamic gestures optically distinct in terms of jaw height, whereas engaging the lips selectively facilitated perception of the dynamic gestures optically distinct in terms of their degree of lip compression and protrusion. Thus, participants perceived visible speech movements in relation to the configuration and shape of their own vocal tract (and possibly their ability to produce covert vowel production–like movements). In contrast, engaging the articulators had no effect when the speaking faces did not move, suggesting that the somatosensory inputs affected perception of time-varying kinematic information rather than changes in target (movement end point) mouth shapes. Conclusions These findings suggest that orofacial somatosensory inputs associated with speech production prime premotor and somatosensory brain regions involved in the sensorimotor control of speech, thereby facilitating perception of concordant visible speech movements. Supplemental Material https://doi.org/10.23641/asha.9911846

Neuroimaging has revealed a core network of cortical regions that contribute to speech production, but the functional organization of this network remains poorly understood. Purpose We describe efforts to identify reliable boundaries around functionally homogenous regions within the cortical speech motor control network in order to improve the sensitivity of functional magnetic resonance imaging (fMRI) analyses of speech production and thus improve our understanding of the functional organization of speech production in the brain. Method We used a bottom-up, data-driven approach by pooling data from 12 previously conducted fMRI studies of speech production involving the production of monosyllabic and bisyllabic words and pseudowords that ranged from single vowels and consonant–vowel pairs to short sentences (163 scanning sessions, 136 unique participants, 39 different speech conditions). After preprocessing all data through the same pipeline and registering individual contrast maps to a common surface space, hierarchical clustering was applied to contrast maps randomly sampled from the pooled data set in order to identify consistent functional boundaries across subjects and tasks. Boundary completion was achieved by applying adaptive smoothing and watershed segmentation to the thresholded population-level boundary map. Hierarchical clustering was applied to the mean within–functional region of interest (fROI) response to identify networks of fROIs that respond similarly during speech. Results We identified highly reliable functional boundaries across the cortical areas involved in speech production. Boundary completion resulted in 117 fROIs in the left hemisphere and 109 in the right hemisphere. Clustering of the mean within-fROI response revealed a core sensorimotor network flanked by a speech motor planning network. The majority of the left inferior frontal gyrus clustered with the visual word form area and brain regions (e.g., anterior insula, dorsal anterior cingulate) associated with detecting salient sensory inputs and choosing the appropriate action. Conclusion The fROIs provide insight into the organization of the speech production network and a valuable tool for studying speech production in the brain by improving within-group and between-groups comparisons of speech-related brain activity. Supplemental Material https://doi.org/10.23641/asha.9402674

PurposeUnderstanding what limits speech development in minimally verbal (MV) children with autism spectrum disorder (ASD) is important for providing highly effective targeted therapies. This preliminary investigation explores the extent to which developmental speech deficits predicted by Directions Into Velocities of Articulators (DIVA), a computational model of speech production, exemplify real phenotypes.MethodImplementing a motor speech disorder in DIVA predicted that speech would become highly variable within and between tokens, while implementing a motor speech plus an auditory processing disorder predicted that DIVA's speech would become highly centralized (schwa-like). Acoustic analyses of DIVA's output predicted that acoustically measured phoneme distortion would be similar between the two cases, but that in the former case, speech would show more within- and between-token variability than in the latter case. We tested these predictions quantitatively on the speech of children with MV ASD. In Study 1, we tested the qualitative predictions using perceptual analysis methods. Speech pathologists blinded to the purpose of the study tallied the signs of childhood apraxia of speech that appeared in the speech of 38 MV children with ASD. K-means clustering was used to create two clusters from the group of 38, and analysis of variance was used to determine whether the clusters differed according to perceptual features corresponding to within- and between-token variability. In Study 2, we employed acoustic analyses on the speech of the child from each cluster who produced the largest number of analyzable tokens to test the predictions of differences in within-token variability, between-token variability, and vowel space area.ResultsClusters produced by k-means analysis differed by perceptual features that corresponded to within-token variability. Nonsignificant differences between clusters were found for features corresponding to between-token variability. Subsequent acoustic analyses of the selected cases revealed that the speech of the child from the high-variability cluster showed significantly more quantitative within- and between-token variability than the speech of the child from the low-variability cluster. The vowel space of the child from the low-variability cluster was more centralized than that of typical children and that of the child from the high-variability cluster.ConclusionsResults provide preliminary evidence that subphenotypes of children with MV ASD may exist, characterized by (a) comorbid motor speech disorder and (b) comorbid motor speech plus auditory processing disorder. The results motivate testable predictions about how these comorbidities affect speech.Supplemental Materialhttps://doi.org/10.23641/asha.14384432

BACKGROUND: Communication difficulties are a core deficit in many people with autism spectrum disorder (ASD). The current study evaluated neural activation in participants with ASD and neurotypical (NT) controls during a speech production task. METHODS: Neural activities of participants with ASD (N = 15, M = 16.7 years, language abilities ranged from low verbal abilities to verbally fluent) and NT controls (N = 12, M = 17.1 years) was examined using functional magnetic resonance imaging with a sparse-sampling paradigm. RESULTS: There were no differences between the ASD and NT groups in average speech activation or inter-subject run-to-run variability in speech activation. Intra-subject run-to-run neural variability was greater in the ASD group and was positively correlated with autism severity in cortical areas associated with speech. CONCLUSIONS: These findings highlight the importance of understanding intra-subject neural variability in participants with ASD.