Stuttering as a trait or state – an <scp>ALE</scp> meta‐analysis of neuroimaging studies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".