Neural Modeling and Imaging of the Cortical Interactions Underlying Syllable Production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production with and without jaw perturbations.National Institute on Deafness and Other Communication Disorders (R01 DC02852, RO1 DC01925

Exploring auditory-motor interactions in normal and disordered speech

Auditory feedback plays an important role in speech motor learning and in the online correction of speech movements. Speakers can detect and correct auditory feedback errors at the segmental and suprasegmental levels during ongoing speech. The frontal brain regions that contribute to these corrective movements have also been shown to be more active during speech in persons who stutter (PWS) compared to fluent speakers. Further, various types of altered auditory feedback can temporarily improve the fluency of PWS, suggesting that atypical auditory-motor interactions during speech may contribute to stuttering disfluencies. To investigate this possibility, we have developed and improved Audapter, a software that enables configurable dynamic perturbation of the spatial and temporal content of the speech auditory signal in real time. Using Audapter, we have measured the compensatory responses of PWS to static and dynamic perturbations of the formant content of auditory feedback and compared these responses with those from matched fluent controls. Our findings indicate deficient utilization of auditory feedback by PWS for short-latency online control of the spatial and temporal parameters of articulation during vowel production and during running speech. These findings provide further evidence that stuttering is associated with aberrant auditory-motor integration during speech.Published versio

Mindboggle: Automated brain labeling with multiple atlases

BACKGROUND: To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data. METHODS: Each brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently. RESULTS: When Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions. CONCLUSION: Increasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images

Behavioral, computational, and neuroimaging studies of acquired apraxia of speech

A critical examination of speech motor control depends on an in-depth understanding of network connectivity associated with Brodmann areas 44 and 45 and surrounding cortices. Damage to these areas has been associated with two conditions-the speech motor programming disorder apraxia of speech (AOS) and the linguistic/grammatical disorder of Broca's aphasia. Here we focus on AOS, which is most commonly associated with damage to posterior Broca's area (BA) and adjacent cortex. We provide an overview of our own studies into the nature of AOS, including behavioral and neuroimaging methods, to explore components of the speech motor network that are associated with normal and disordered speech motor programming in AOS. Behavioral, neuroimaging, and computational modeling studies are indicating that AOS is associated with impairment in learning feedforward models and/or implementing feedback mechanisms and with the functional contribution of BA6. While functional connectivity methods are not yet routinely applied to the study of AOS, we highlight the need for focusing on the functional impact of localized lesions throughout the speech network, as well as larger scale comparative studies to distinguish the unique behavioral and neurological signature of AOS. By coupling these methods with neural network models, we have a powerful set of tools to improve our understanding of the neural mechanisms that underlie AOS, and speech production generally

Representation of Sound Categories in Auditory Cortical Maps

We used functional magnetic resonance imaging (fMRI) to investigate the representation of sound categories in human auditory cortex. Experiment 1 investigated the representation of prototypical and non-prototypical examples of a vowel sound. Listening to prototypical examples of a vowel resulted in less auditory cortical activation than listening to nonprototypical examples. Experiments 2 and 3 investigated the effects of categorization training and discrimination training with novel non-speech sounds on auditory cortical representations. The two training tasks were shown to have opposite effects on the auditory cortical representation of sounds experienced during training: discrimination training led to an increase in the amount of activation caused by the training stimuli, whereas categorization training led to decreased activation. These results indicate that the brain efficiently shifts neural resources away from regions of acoustic space where discrimination between sounds is not behaviorally important (e.g., near the center of a sound category) and toward regions where accurate discrimination is needed. The results also provide a straightforward neural account of learned aspects of categorical perception: sounds from the center of a category are more difficult to discriminate from each other than sounds near category boundaries because they are represented by fewer cells in the auditory cortical areas.National Institute on Deafness and Other Communication Disorders (R01 DC02852

ROI-Based Analysis of Functional Imaging Data

In this technical report, we present fMRI analysis techniques that test functional hypotheses at the region of interest (ROI) level. An SPM-compatible Matlab toolbox has been developed which allows the creation of subject-specific ROI masks based on anatomical markers and the testing of functional hypotheses on the regional response using multivariate time-series analysis techniques. The combined application of subject-specific ROI definition and region-level functional analysis is shown to appropriately compensate for inter-subject anatomical variability, offering finer localization and increased sensitivity to task-related effects than standard techniques based on whole brain normalization and voxel or cluster-level functional analysis, while providing a more direct link between discrete brain region hypotheses and the statistical analyses used to test them.National Institute of Health (R29 DC02852, ROI DC02852

Reliability of single-subject neural activation patterns in speech production tasks

Traditional group fMRI (functional magnetic resonance imaging) analyses are not designed to detect individual differences that may be crucial to better understanding speech disorders. Single-subject research could therefore provide a richer characterization of the neural substrates of speech production in development and disease. Before this line of research can be tackled, however, it is necessary to evaluate whether healthy individuals exhibit reproducible brain activation across multiple sessions during speech production tasks. In the present study, we evaluated the reliability and discriminability of cortical functional magnetic resonance imaging data from twenty neurotypical subjects who participated in two experiments involving reading aloud mono- or bisyllabic speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability, with exceptions likely due to increased head motion in the scanner. Further, this level of reliability for speech production was not directly correlated with reliable patterns in the underlying average blood oxygenation level dependent signal across the brain. Finally, we found that a novel machine-learning subject classifier could identify these individuals by their speech activation patterns with 97% accuracy from among a dataset of seventy-five subjects. These results suggest that single-subject speech research would yield valid results and that investigations into the reliability of speech activation in people with speech disorders are warranted.Accepted manuscrip

The neural correlates of speech motor sequence learning

Speech is perhaps the most sophisticated example of a species-wide movement capability in the animal kingdom, requiring split-second sequencing of approximately 100 muscles in the respiratory, laryngeal, and oral movement systems. Despite the unique role speech plays in human interaction and the debilitating impact of its disruption, little is known about the neural mechanisms underlying speech motor learning. Here, we studied the behavioral and neural correlates of learning new speech motor sequences. Participants repeatedly produced novel, meaningless syllables comprising illegal consonant clusters (e.g., GVAZF) over 2 days of practice. Following practice, participants produced the sequences with fewer errors and shorter durations, indicative of motor learning. Using fMRI, we compared brain activity during production of the learned illegal sequences and novel illegal sequences. Greater activity was noted during production of novel sequences in brain regions linked to non-speech motor sequence learning, including the BG and pre-SMA. Activity during novel sequence production was also greater in brain regions associated with learning and maintaining speech motor programs, including lateral premotor cortex, frontal operculum, and posterior superior temporal cortex. Measures of learning success correlated positively with activity in left frontal operculum and white matter integrity under left posterior superior temporal sulcus. These findings indicate speech motor sequence learning relies not only on brain areas involved generally in motor sequencing learning but also those associated with feedback-based speech motor learning. Furthermore, learning success is modulated by the integrity of structural connectivity between these motor and sensory brain regions.R01 DC007683 - NIDCD NIH HHS; R01DC007683 - NIDCD NIH HH

Anomalous morphology in left hemisphere motor and premotor cortex of children who stutter

Stuttering is a neurodevelopmental disorder that affects the smooth flow of speech production. Stuttering onset occurs during a dynamic period of development when children first start learning to formulate sentences. Although most children grow out of stuttering naturally, ∼1% of all children develop persistent stuttering that can lead to significant psychosocial consequences throughout one’s life. To date, few studies have examined neural bases of stuttering in children who stutter, and even fewer have examined the basis for natural recovery versus persistence of stuttering. Here we report the first study to conduct surface-based analysis of the brain morphometric measures in children who stutter. We used FreeSurfer to extract cortical size and shape measures from structural MRI scans collected from the initial year of a longitudinal study involving 70 children (36 stuttering, 34 controls) in the 3–10-year range. The stuttering group was further divided into two groups: persistent and recovered, based on their later longitudinal visits that allowed determination of their eventual clinical outcome. A region of interest analysis that focused on the left hemisphere speech network and a whole-brain exploratory analysis were conducted to examine group differences and group × age interaction effects. We found that the persistent group could be differentiated from the control and recovered groups by reduced cortical thickness in left motor and lateral premotor cortical regions. The recovered group showed an age-related decrease in local gyrification in the left medial premotor cortex (supplementary motor area and and pre-supplementary motor area). These results provide strong evidence of a primary deficit in the left hemisphere speech network, specifically involving lateral premotor cortex and primary motor cortex, in persistent developmental stuttering. Results further point to a possible compensatory mechanism involving left medial premotor cortex in those who recover from childhood stuttering.This study was supported by Award Numbers R01DC011277 (SC) and R01DC007683 (FG) from the National Institute on Deafness and other Communication Disorders (NIDCD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDCD or the National Institutes of Health. (R01DC011277 - National Institute on Deafness and other Communication Disorders (NIDCD); R01DC007683 - National Institute on Deafness and other Communication Disorders (NIDCD))Accepted manuscrip

Auditory feedback control mechanisms do not contribute to cortical hyperactivity within the voice production network in adductor spasmodic dysphonia

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 ADSD participants 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. To test this possibility, functional magnetic resonance imaging was used to identify differences between ADSD participants and age-matched controls in (i) brain activity when producing speech under different auditory feedback conditions, and (ii) resting state functional connectivity within the cortical network responsible for vocalization. The ADSD group had significantly higher activity than the control group during speech (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. This was true for speech while auditory feedback was masked with noise as well as for speech with normal auditory feedback, indicating that the excess activity was not the result of auditory feedback control mechanisms attempting to correct for perceived voicing errors in ADSD. 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, consistent with the view that excessive motor activity frequently co-occurs with increased auditory cortical activity in individuals with ADSD.First author draf