The Current and Future Potential of Transcranial Magnetic Stimulation With Electroencephalography in Psychiatry
Bibliographic record
Abstract
The search for biological targets in psychiatric disorders is essential to better understand illness mechanisms and also to monitor and predict response to currently available therapeutic interventions. To this end, the combination of transcranial magnetic stimulation with electroencephalography (TMS-EEG) has emerged as a powerful clinical research tool. TMS-EEG allows cortical properties, such as excitability, inhibition, oscillatory activity, and connectivity, to be directly probed within a specific region of the cortex. This review will summarize the state of the current literature on TMS-EEG and its potential to uncover biological targets in psychiatric illnesses, with a focus on major depressive disorder, bipolar disorder, and schizophrenia. Collectively, the reviewed studies suggest that alterations in gamma-aminobutyric acid-mediated inhibition and gamma oscillations in the dorsolateral prefrontal cortex and neighboring frontal regions are potential shared biomarkers in psychiatry, highlighting the potential of TMS-EEG to help identify translational biomarkers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".