Cerebellar Transcranial Alternating Current Stimulation Accelerates Motor Adaptation via the Modulation of Cortical Synchrony
Bibliographic record
Abstract
Motor adaptation is a form of motor learning that enables the updating of motor commands in response to sensory inputs, requiring computations at the cerebellar level that must be integrated into cerebral cortical networks for their implementation. We proposed that cerebellar-cortical integration, which underlies motor adaptation, is related to the modulation of frequency-specific oscillatory activity. We examined motor error and electrophysiological correlates (power spectrum and phase locking value analysis) measured during different sessions of transcranial alternating stimulation (tACS) delivered to the cerebellum at relevant frequencies (50 Hz, 20 Hz, or sham). We found that 50 Hz tACS, but not 20 Hz or sham stimulation, reduced movement error, especially in initial practice trials. Electroencephalography (EEG) analysis revealed modulation of spectral power and phase synchrony (wPLI) in frontal, parietal, and occipital regions, with specific patterns for both the frequency range and the task stage. Power and wPLI modulation under fifty Hz stimulation were associated with the magnitude of motor adaptation. Our findings suggest that frequency-specific neural oscillations play a crucial role in the effective integration between the cerebellum and cortical regions of the brain. Significance: Our data indicate that cerebellar tACS at approximately 50 Hz may serve as an effective neuromodulation strategy to enhance motor adaptation in humans, with specific neural correlates.
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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".