Transcranial magnetic stimulation on the modulation of gamma oscillations in schizophrenia
Bibliographic record
Abstract
Cognitive dysfunction is suggested to be the best predictor of functional outcome in schizophrenia. Therefore, new diagnostic and treatment strategies are needed to both ascertain the biological underpinning of cognitive deficits and to restore them. Modulation of gamma oscillations (30-50 Hz) has been associated with cognitive performance, particularly in the dorsolateral prefrontal cortex (DLPFC). In this manuscript, we review evidence for gamma modulation deficits during cognitive performance in schizophrenia. We demonstrate that transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a reliable method that permits systematic quantification of gamma modulation in the cortex. Using TMS-EEG, we show that patients with schizophrenia have selective gamma inhibition deficits in the DLPFC. Finally, we demonstrate that repetitive TMS therapy over the DLPFC can normalize excessive gamma oscillations and ultimately cognitive performance in patients. We suggest that restoring gamma impairments in the DLPFC may be a potential strategy for improving cognitive deficits in schizophrenia.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".