Bibliographic record
Abstract
Abstract Theta burst stimulation (TBS), a form of repetitive transcranial magnetic stimulation (TMS), is capable of non-invasively modulating cortical excitability. TBS is gaining popularity as a therapeutic tool for psychiatric disorders such as depression, in which the dorsolateral prefrontal cortex (DLPFC) is the main therapeutic target. However, the neuromodulatory effects of TBS on prefrontal regions remain unclear. An emerging tool to assess neuromodulation in non-motor regions is concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) to measure TMS-evoked potentials (TEPs). We assessed twenty-four healthy participants (13 males, mean age 25.2±9.9 years) following intermittent TBS, continuous TBS, and sham applied to the left DLPFC using a double-blinded crossover design. TEPs were obtained at baseline and 2-, 15-, and 30-min post-stimulation. Four TEP components (N40, P60, N100 and P200) were analysed using mixed effects repeated measures models (MRMM). Results indicate no significant effects for any assessed components (all p>.05). The largest effect size (Cohen’s d = −0.5) comparing iTBS and sham was obtained for the N100 component at 15 minutes post-stimulation. This result was in the same direction but smaller than found in previous studies, suggesting that the true effect size may be lower than previously reported. Accurate estimates of the effects sizes and inter-individual heterogeneity will critically inform clinical applications using TEPs to assess the neuromodulatory effects of TBS.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.011 |
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".