No abstract

This paper investigates the hypothesis that stuttering may result in part from impaired readout of feedforward control of speech, which forces persons who stutter (PWS) to produce speech with a motor strategy that is weighted too much toward auditory feedback control. Over-reliance on feedback control leads to production errors which, if they grow large enough, can cause the motor system to "reset" and repeat the current syllable. This hypothesis is investigated using computer simulations of a "neurally impaired" version of the DIVA model, a neural network model of speech acquisition and production. The model's outputs are compared to published acoustic data from PWS' fluent speech, and to combined acoustic and articulatory movement data collected from the dysfluent speech of one PWS. The simulations mimic the errors observed in the PWS subject's speech, as well as the repairs of these errors. Additional simulations were able to account for enhancements of fluency gained by slowed/prolonged speech and masking noise. Together these results support the hypothesis that many dysfluencies in stuttering are due to a bias away from feedforward control and toward feedback control. EDUCATIONAL OBJECTIVES The reader will be able to (a) describe the contribution of auditory feedback control and feedforward control to normal and stuttered speech production, (b) understand the neural modeling approach to speech production and its application to stuttering, and (c) explain how the DIVA model accounts for enhancements of fluency gained by slowed/prolonged speech and masking noise.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers

A typical white-matter integrity and elevated dopamine levels have been reported for individuals who stutter. We investigated how such abnormalities may lead to speech dysfluencies due to their effects on a syllable-sequencing circuit that consists of basal ganglia (BG), thalamus, and left ventral premotor cortex (vPMC). “Neurally impaired” versions of the neurocomputational speech production model GODIVA were utilized to test two hypotheses: (1) that white-matter abnormalities disturb the circuit via corticostriatal projections carrying copies of executed motor commands, and (2) that dopaminergic abnormalities disturb the circuit via the striatum. Simulation results support both hypotheses: in both scenarios, the neural abnormalities delay readout of the next syllable’s motor program, leading to dysfluency. The results also account for brain imaging findings during dysfluent speech. It is concluded that each of the two abnormality types can cause stuttering moments, probably by affecting the same BG-thalamus-vPMC circuit.

The frontal aslant tract (FAT) is a pathway that connects the inferior frontal gyrus with the supplementary motor area (SMA) and pre-SMA. The FAT was recently identified and introduced as part of a "motor stream" that plays an important role in speech production. In this study, we use diffusion imaging to examine the hypothesis that the FAT underlies speech fluency, by studying its properties in individuals with persistent developmental stuttering, a speech disorder that disrupts the production of fluent speech. We use tractography to quantify the volume and diffusion properties of the FAT in a group of adults who stutter (AWS) and fluent controls. Additionally, we use tractography to extract these measures from the corticospinal tract (CST), a well-known component of the motor system. We compute diffusion measures in multiple points along the tracts, and examine the correlation between these diffusion measures and behavioral measures of speech fluency. Our data show increased mean diffusivity in bilateral FAT of AWS compared with controls. In addition, the results show regions within the left FAT and the left CST where diffusivity values are increased in AWS compared with controls. Last, we report that in AWS, diffusivity values measured within sub-regions of the left FAT negatively correlate with speech fluency. Our findings are the first to relate the FAT with fluent speech production in stuttering, thus adding to the current knowledge of the functional role that this tract plays in speech production and to the literature of the etiology of persistent developmental stuttering.

There are widespread abnormalities in the structural architecture and functional organization of the brains of adults and children who stutter. These are evident not only in speech tasks, but also non-speech tasks. Future research should make greater use of functional neuroimaging and noninvasive brain stimulation, and employ structural methodologies that have greater sensitivity. Newly planned studies should also investigate sex differences, focus on augmenting treatment, examine moments of dysfluency and longitudinally or cross-sectionally investigate developmental trajectories in stuttering.

Recent advances in diffusion MRI tractography permit the generation of dense weighted structural connectomes that offer greater insight into brain organization. However, these efforts are hampered by the lack of consensus on how to extract topological measures from the resulting graphs. Here we evaluate the common practice of removing the graphs' weak connections, which is primarily intended to eliminate spurious connections and emphasize strong connections. Because this processing step requires arbitrary or heuristic-based choices (e.g., setting a threshold level below which connections are removed), and such choices might complicate statistical analysis and inter-study comparisons, in this work we test whether removing weak connections is indeed necessary. To this end, we systematically evaluated the effect of removing weak connections on a range of popular graphtheoretical metrics. Specifically, we investigated if (and at what extent) removal of weak connections introduces a statistically significant difference between two otherwise equal groups of healthy subjects when only applied to one of the groups. Using data from the Human Connectome Project, we found that removal of weak connections had no statistical effect even when removing the weakest ~70-90% connections. Removing yet a larger extent of weak connections, thus reducing connectivity density even further, did produce a predictably significant effect. However, metric values became sensitive to the exact connectivity density, which has ramifications regarding the stability of the statistical analysis. This pattern persisted whether connections were removed by connection strength threshold or connectivity density, and for connectomes generated using parcellations at different resolutions. Finally, we showed that the same pattern also applies for data from a clinical-grade MRI scanner. In conclusion, our analysis revealed that removing weak connections is not necessary for graph-theoretical analysis of dense weighted connectomes. Because removal of weak connections provides no practical utility to offset the undesirable requirement for arbitrary or heuristic-based choices, we recommend that this step is avoided in future studies.

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organisation. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "fixel-based analysis" (FBA) framework that implements bespoke solutions to this end, and has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to fixel-based analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of current fixel-based analysis studies (until August 2020), categorised across a broad range of neuroscientific domains, listing key design choices and summarising their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the fixel-based analysis framework, and outline some directions and future opportunities.

No abstract