Stuttering is a speech disorder characterised by repetitions, prolongations and blocks that disrupt the forward movement of speech. An earlier meta-analysis of brain imaging studies of stuttering (Brown et al., 2005) revealed a general trend towards rightward lateralization of brain activations and hyperactivity in the larynx motor cortex bilaterally. The present study sought not only to update that meta-analysis with recent work but to introduce an important distinction not present in the first study, namely the difference between 'trait' and 'state' stuttering. The analysis of trait stuttering compares people who stutter (PWS) with people who do not stutter when behaviour is controlled for, i.e., when speech is fluent in both groups. In contrast, the analysis of state stuttering examines PWS during episodes of stuttered speech compared with episodes of fluent speech. Seventeen studies were analysed using activation likelihood estimation. Trait stuttering was characterised by the well-known rightward shift in lateralization for language and speech areas. State stuttering revealed a more diverse pattern. Abnormal activation of larynx and lip motor cortex was common to the two analyses. State stuttering was associated with overactivation in the right hemisphere larynx and lip motor cortex. Trait stuttering was associated with overactivation of lip motor cortex in the right hemisphere but underactivation of larynx motor cortex in the left hemisphere. These results support a large literature highlighting laryngeal and lip involvement in the symptomatology of stuttering, and disambiguate two possible sources of activation in neuroimaging studies of persistent developmental stuttering.
Objective: The literature on the genetics of stuttering is reviewed with special reference to the historical development from psychosocial explanations leading up to current biological research of gene identification. Summary: A gradual progression has been made from the early crude methods of counting percentages of stuttering probands who have relatives who stutter to recent studies using entire genomes of DNA collected from each participant. Despite the shortcomings of some early studies, investigators have accumulated a substantial body of data showing a large presence of familial stuttering. This encouraged more refined research in the form of twin studies. Concordance rates among twins were sufficiently high to lend additional support to the genetic perspective of stuttering. More sophisticated aggregation studies and segregation analyses followed, producing data that matched recognized genetic models, providing the final ‘go ahead’ to proceed from the behavior/statistical genetics into the sphere of biological genetics. Recent linkage and association studies have begun to reveal contributing genes to the disorder. Conclusion: No definitive findings have been made regarding which transmission model, chromosomes, genes, or sex factors are involved in the expression of stuttering in the population at large. Future research and clinical implications are discussed.
Individual variability in response to DAF may be accounted for by subgroups of individuals. This suggests that certain normally fluent individuals could be more dependent on intact feedback to maintain fluency.
Purpose: This study aimed to identify cases of developmental stuttering and associated comorbidities in de-identified electronic health records (EHRs) at Vanderbilt University Medical Center, and, in turn, build and test a stuttering prediction model. Methods: A multi-step process including a keyword search of medical notes, a text-mining algorithm, and manual review was employed to identify stuttering cases in the EHR. Confirmed cases were compared to matched controls in a phenotype code (phecode) enrichment analysis to reveal conditions associated with stuttering (i.e., comorbidities). These associated phenotypes were used as proxy variables to phenotypically predict stuttering in subjects within the EHR that were not otherwise identifiable using the multi-step identification process described above. Results: The multi-step process resulted in the manually reviewed identification of 1,143 stuttering cases in the EHR. Highly enriched phecodes included codes related to childhood onset fluency disorder, adult-onset fluency disorder, hearing loss, sleep disorders, atopy, a multitude of codes for infections, neurological deficits, and body weight. These phecodes were used as variables to create a phenome risk classifier (PheRC) prediction model to identify additional high likelihood stuttering cases. The PheRC prediction model resulted in a positive predictive value of 83 %. Conclusions: This study demonstrates the feasibility of using EHRs in the study of stuttering and found phenotypic associations. The creation of the PheRC has the potential to enable future studies of stuttering using existing EHR data, including investigations into the genetic etiology.
Recent studies have implicated anatomical differences in speech-relevant brain regions of adults who stutter (AWS) compared to normally fluent adults (NFA). The present study focused on the region of the corpus callosum (CC) which is involved in interhemispheric processing between the left and right cerebral hemispheres. Two-dimensional segmentation of area and voxel based morphometry were used to evaluate the corpus callosum. Results revealed that the rostrum and anterior midbody of the CC was larger in AWS than NFA. In addition, the overall callosa area was larger in AWS than NFA. The group comparison of white matter volume showed a cluster of increased white matter volume predominantly encompassing the rostrum across the midline portion in AWS. These results potentially reflect anatomical changes associated with differences in the hemispheric distribution of language processes that has been reported previously in AWS. Educational objectives After reading this article, the reader will be able to: (1) summarize research findings on functional and anatomical differences between AWS and NFA; (2) summarize research findings on anatomical anomalies observed in AWS; (3) discuss the possible relationships between functional and anatomical aberrations in AWS; and (4) discuss how the findings of the present study may support results of previous behavioral investigations (e.g. dichotic listening) in AWS.
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