Abstract
Neural network-based investigations of stuttering have begun to provide a possible integrative account for the large number of brain-based anomalies associated with stuttering. Here we used resting-state EEG to investigate functional brain networks in adults who stutter (AWS). Participants were 19 AWS and 52 age-, and gender-matched normally fluent speakers. EEGs were recorded and connectivity matrices were generated by LORETA in the theta (4–8 Hz), alpha (8–12 Hz), beta1 (12–20 Hz), and beta2 (20–30 Hz) bands. Small-world propensity (SWP), shortest path, and clustering coefficients were computed for weighted graphs. Minimum spanning tree analysis was also performed and measures were compared by non-parametric permutation test. The results show that small-world topology was evident in the functional networks of all participants. Three graph indices (diameter, clustering coefficient, and shortest path) exhibited significant differences between groups in the theta band and one [maximum betweenness centrality (BC)] measure was significantly different between groups in the beta2 band. AWS show higher BC than control in right temporal and inferior frontal areas and lower BC in the right primary motor cortex. Abnormal functional networks during rest state suggest an anomaly of DMN activity in AWS. Furthermore, functional segregation/integration deficits in the theta network are evident in AWS. These deficits reinforce the hypothesis that there is a neural basis for abnormal executive function in AWS. Increased beta2 BC in the right speech–motor related areas confirms previous evidence that right audio–speech areas are over-activated in AWS. Decreased beta2 BC in the right primary motor cortex is discussed in relation to abnormal neural mechanisms associated with time perception in AWS.
Highlights
Stuttering is a developmental disorder of speech fluency that affects 1% of all adults (Craig et al, 2002)
Stuttering is related to several abnormalities in cortical and subcortical brain areas such as the Broca’s area (BA 44, 45), the basal ganglia, supplementary motor area, and parasylvian cortex (Gordon, 2002; Büchel and Sommer, 2004; Chang et al, 2009; Loucks et al, 2011; Sowman et al, 2017) which can be linked to mechanistic explanations proposed to account for stuttering such as auditory–speech dysfunction (Liotti et al, 2010; Jansson-Verkasalo et al, 2014) and a speech–motor impairment (Neilson and Neilson, 1987)
After False Discovery Rate (FDR) correction, three graph indices exhibited significant differences between groups in the theta band and one measure was significantly different between groups in the beta2 band
Summary
Stuttering is a developmental disorder of speech fluency that affects 1% of all adults (Craig et al, 2002). Stuttering is associated with abnormalities in complex cognitive functions such as language (Weber-Fox and Hampton, 2008), motor preparation (Mersov et al, 2016), time perception (Ezrati-Vinacour and Levin, 2001), and attention (Kamhi and McOsker, 1982). Over the last two decades, a body of neuroimaging research has amassed which suggests that stuttering likely emerges from deficiencies in the brain mechanisms that support fluent speech production (e.g., Chang et al, 2009, 2017). Neural network-based investigations of stuttering have begun to provide a possible integrative account that might account for the large number of brain-based anomalies (for review see: Brown et al, 2005; Budde et al, 2014; Belyk et al, 2015; Etchell et al, 2017) associated with stuttering
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