Deep Brain Stimulation Improves Symptoms of Spasmodic Dysphonia Through Targeting of Thalamic Sensorimotor Connectivity
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Spasmodic dysphonia is a dystonia of the vocal chords producing difficulty with speech. Current hypotheses are that this is a condition of dysregulated thalamic sensory motor integration. A recent randomized controlled trial of thalamic deep brain stimulation (DBS) demonstrated its safety and efficacy. Our objective was to determine whether the outcome could be predicted by stimulation of thalamic sensorimotor areas and adjacent white matter connectivity as assessed by diffusion tractography. METHODS: A cohort of 6 participants undergoing thalamic DBS for adductor spasmodic dysphonia was studied. Electrodes were localized with the Lead-DBS toolbox. Group-based analyses were performed with atlases, coordinates, and using voxel-based symptom mapping. Diffusion tensor imaging (3 T, 64 directions, 2-mm isotropic) was used to perform individual probabilistic tractography (cerebellothalamic tract and pallidothalamic tract) and segmentation of the thalamus. Monopolar review was performed at 0.5 V and binarised as effective or ineffective. RESULTS: Effective contacts stimulated more of thalamic sensorimotor areas than ineffective contacts ( P < .05, false discovery rate corrected). This effect was consistent across analytical and statistical techniques. Group-level and tractography analyses did not identify a specific "sweet spot" suggesting the benefit of DBS is derived from modulating individual thalamic sensorimotor areas. Stimulations at 1 year involved predicted thalamic sensorimotor regions with additional cerebellothalamic tract involvement. CONCLUSION: Stimulation of thalamic sensorimotor areas was associated with improvement in symptoms of spasmodic dysphonia. These data are consistent with DBS acting on pathophysiologically dysregulated thalamic sensorimotor integration in spasmodic dysphonia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".