Changes in White Matter Integrity following Intensive Voice Treatment (LSVT LOUD®) in Children with Cerebral Palsy and Motor Speech Disorders
Bibliographic record
Abstract
Preliminary evidence suggests that intensive voice and speech treatment based on activity-dependent neuroplasticity principles holds promise for affecting positive change in children with cerebral palsy (CP) and motor speech disorders. Diffusion tensor imaging (DTI) allows researchers to make inferences about the integrity of white matter tracks and provides a sensitive measure of neuroplasticity. Previous treatment studies looking at the effects of training on white matter integrity have shown positive results, but these studies have been limited to gross motor function. Eight children with motor speech disorders and CP (3 females; age 8-16 years) and an age- and sex-matched group of typically developing (TD) children participated. Each child with CP completed a full dose of LSVT LOUD® and a 12-week maintenance program. Participants attended 3 recording sessions: before and after treatment, and after the maintenance period. TD children were tested at the same 3 time points. Recording sessions for both groups of children included measures of white matter integrity using DTI and acoustic measures of voice and speech. Fractional anisotropy (FA) was measured for 2 motor tracts and 5 association tracts. In children with CP, we observed an increase in FA in several motor and association tracts immediately following treatment and 12 weeks after treatment. Acoustic data on untrained tasks were correlated with changes in FA detected immediately following treatment and after the 12-week maintenance program. These findings suggest that long-term practice of skills attained during the treatment phase enhances white matter tract integrity in speech production networks.
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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".