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

Surface electromyographic control of a novel phonemic interface for speech synthesis

Many individuals with minimal movement capabilities use AAC to communicate. These individuals require both an interface with which to construct a message (e.g., a grid of letters) and an input modality with which to select targets. This study evaluated the interaction of two such systems: (a) an input modality using surface electromyography (sEMG) of spared facial musculature, and (b) an onscreen interface from which users select phonemic targets. These systems were evaluated in two experiments: (a) participants without motor impairments used the systems during a series of eight training sessions, and (b) one individual who uses AAC used the systems for two sessions. Both the phonemic interface and the electromyographic cursor show promise for future AAC applications.F31 DC014872 - NIDCD NIH HHS; R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HHS; T90 DA032484 - NIDA NIH HHShttps://www.ncbi.nlm.nih.gov/pubmed/?term=Surface+electromyographic+control+of+a+novel+phonemic+interface+for+speech+synthesishttps://www.ncbi.nlm.nih.gov/pubmed/?term=Surface+electromyographic+control+of+a+novel+phonemic+interface+for+speech+synthesisPublished versio

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

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

Speech neuroimaging research targeting individual speakers could help elucidate differences that may be crucial to understanding speech disorders. However, this research necessitates reliable brain activation across multiple speech production sessions. In the present study, we evaluated the reliability of speech-related brain activity measured by functional magnetic resonance imaging data from twenty neuro-typical subjects who participated in two experiments involving reading aloud simple speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability. We also 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.R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HHS; T32 DC013017 - NIDCD NIH HHSAccepted manuscrip