Cerebellar repetitive transcranial magnetic stimulation attenuates essential tremor by modulating visuomotor network connectivity
Bibliographic record
Abstract
OBJECTIVE: To investigate the neurophysiological mechanisms underlying cerebellar repetitive transcranial magnetic stimulation (rTMS) in essential tremor (ET) using source-level electroencephalography (EEG) phase-locking value analysis. METHODS: EEG recordings and clinical assessments of ET patients (n = 20) before and after 4-week bilateral cerebellar rTMS were studied and evaluated for correlations. Brain source activities were reconstructed using standardized low-resolution brain electromagnetic tomography (LORETA). Functional connectivity and local network metrics in the alpha band (8-13 Hz) were compared with those of healthy controls (n = 20). RESULTS: ET patients had lower nodal efficiency in visual areas and reduced inter-regional occipital connectivity than controls. Local network metrics in the left superior occipital gyrus, left cuneus, and right fusiform gyrus negatively correlated with tremor severity and significantly improved with rTMS. Connectivity changes between the left inferior occipital gyrus (IOG) and superior parietal gyrus (SPG) and the right SPG and midcingulate cortex (MCC), strongly correlated with improvements in activities of daily living. Performance-related connectivity changes involved the left IOG-SPG, right SPG-MCC, and left middle occipital gyrus to the supplementary motor area. CONCLUSIONS: Cerebellar rTMS may alleviate ET by reorganizing functional connectivity within the visuomotor network. SIGNIFICANCE: Our findings provide preliminary mechanistic insights and potential biomarkers for ET treatment, pending validation in future sham-controlled studies.
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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".