The Speech Network in Childhood Stuttering: Differences in Functional Connectivity of the Planning and Motor Loops
Bibliographic record
Abstract
Developmental stuttering is a complex neurodevelopmental condition associated with structural and functional anomalies in the basal ganglia-thalamo-cortical (BGTC) circuits that support speech planning and execution. In this study, we examined hypothesized impairments in the planning and motor circuits of the speech network in children who stutter (CWS), compared to children who do not stutter (CNS), using person-specific functional connectivity maps derived from resting-state functional magnetic resonance imaging (rsfMRI) data. RsfMRI data were acquired from 73 CWS and 74 CNS, aged 3 to 10 years. Twelve regions of interest within the speech motor networks were extracted. Functional connectivity was assessed using confirmatory subgrouping group iterative multiple model estimation (CS-GIMME), which estimates group-, subgroup-, and individual-level connections. Subgroup-level functional connectivity patterns revealed altered connections among CWS in both planning and motor loops, including reduced within-network connectivity, compared to CNS. CWS showed connectivity between the left posterior inferior frontal sulcus and left ventral lateral thalamus that was not observed in CNS. Furthermore, centrality of the left ventral lateral thalamus and right ventral premotor cortex were increased in CWS relative to CNS. Significant differences between CWS and CNS in within-network connectivity highlight early developmental alterations that affect the BGTC circuitry, pointing toward inefficiencies in the neural network that supports the programming, planning and timing of speech motor sequences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".