Engaging dystonia networks with subthalamic stimulation
Bibliographic record
Abstract
Deep brain stimulation is an efficacious treatment for dystonia. While the internal pallidum serves as the primary target, recently, stimulation of the subthalamic nucleus (STN) has been investigated. However, optimal targeting within this structure and its surroundings have not been studied in depth. Indeed, historical targets that have been used for surgical treatment of dystonia are directly adjacent to the STN. Further, multiple types of dystonia exist, and outcomes are variable, suggesting that not all types would profit maximally from the same target. Therefore, a thorough investigation of neural substrates underlying stimulation effects on dystonia signs and symptoms is warranted. Here, we analyze a multicenter cohort of isolated dystonia patients with subthalamic implantations ( N = 58) and relate their stimulation sites to improvements of appendicular and cervical symptoms as well as blepharospasm. Stimulation of the ventral oral posterior nucleus of thalamus and surrounding regions were associated with improvements in cervical dystonia, while stimulation of the dorsolateral STN was associated with improvements in limb dystonia and blepharospasm. This dissociation was matched by structural connectivity analysis, where the cerebellothalamic, corticospinal, and pallidosubthalamic tracts were associated with improvements of cervical dystonia, while hyperdirect and subthalamopallidal pathways with alleviation of limb dystonia and blepharospasm. On the level of functional networks, improvements of limb dystonia were associated with connectivity to the corresponding somatotopic regions in the primary motor cortex, while alleviation of cervical dystonia to the cingulo-opercular network. These findings shed light on the pathophysiology of dystonia and may guide DBS targeting and programming in the future.
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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) | 0.001 | 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".