Effects of tACS on alpha oscillations are task-dependent
Bibliographic record
Abstract
Transcranial alternating current stimulation (tACS) is a widely used non-invasive technique that aims to modulate neural activity and behaviour. However, the effects of tACS are rather inconsistent and difficult to replicate. In this study, we seek to modulate alpha oscillations and further research on identifying the optimal conditions for observing reliable tACS-induced changes. Specifically, we investigated the effects of 10 Hz stimulation on alpha oscillations compared to 41 Hz stimulation in a within-subject experimental design. We chose a montage with the anode and cathodes spaced farther apart to generate stronger electric fields at the expense of focality. Participants performed an oddball version of the standard vigilance task with a lateralized bias in motor responses, which is likely to engage alpha oscillations as they are thought to play a role in inhibitory control and lateralized attention management. Our findings revealed that the difference in alpha power enhancement between alpha and gamma stimulation was higher for the contralateral electrodes than the ipsilateral ones. This lateralized effect emerged despite our non-lateralized tACS electrode positioning, suggesting it may be due to the lateralized inhibitory motor planning involved in the task. Our results indicate that the effects of tACS on alpha oscillations are task-specific. Consequently, tACS researchers may opt for stronger electric fields, even at the cost of focality, as tasks could help direct tACS effects to specific brain areas.
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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 | 0.003 |
| 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".