Slow-oscillatory tACS does not modulate human motor cortical response to repeated plasticity paradigms
Bibliographic record
Abstract
Previous history of activity and learning modulates synaptic plasticity and can lead to saturation of synaptic connections. According to the synaptic homeostasis hypothesis, neural oscillations during slow-wave sleep play an important role in restoring plasticity within a functional range. However, it is not known whether slow-wave oscillations-without the concomitant requirement of sleep-play a causal role in human synaptic homeostasis. Here, we aimed to answer this question using transcranial alternating current stimulation (tACS) to induce slow-oscillatory activity in awake human participants. tACS was interleaved between two plasticity-inducing interventions: motor learning, and paired associative stimulation (PAS). The hypothesis tested was that slow-oscillatory tACS would prevent homeostatic interference between motor learning and PAS, and facilitate plasticity from these successive interventions. Thirty-six participants received sham and active fronto-motor tACS in two separate sessions, along with electroencephalography (EEG) recordings, while a further 38 participants received tACS through a control montage. Motor evoked potentials (MEPs) were recorded throughout the session to quantify plasticity changes after the different interventions, and the data were analysed with Bayesian statistics. As expected, there was converging evidence that motor training led to excitatory plasticity. Importantly, we found moderate evidence against an effect of active tACS in restoring PAS plasticity, and no evidence of lasting entrainment of slow oscillations in the EEG. This suggests that, under the conditions tested here, slow-oscillatory tACS does not modulate synaptic homeostasis in the motor system of awake humans.
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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 | 0.000 | 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".