The Effect of Repetitive Transcranial Magnetic Stimulation on Gamma Oscillatory Activity in Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Gamma (γ) oscillations (30-50 Hz) have been shown to be excessive in patients with schizophrenia (SCZ) during working memory (WM). WM is a cognitive process that involves the online maintenance and manipulation of information that is mediated largely by the dorsolateral prefrontal cortex (DLPFC). Repetitive transcranial magnetic stimulation (rTMS) represents a non-invasive method to stimulate the cortex that has been shown to enhance cognition and γ oscillatory activity during WM. METHODOLOGY AND PRINCIPAL FINDINGS: We examined the effect of 20 Hz rTMS over the DLPFC on γ oscillatory activity elicited during the N-back task in 24 patients with SCZ compared to 22 healthy subjects. Prior to rTMS, patients with SCZ elicited excessive γ oscillatory activity compared to healthy subjects across WM load. Active rTMS resulted in the reduction of frontal γ oscillatory activity in patients with SCZ, while potentiating activity in healthy subjects in the 3-back, the most difficult condition. Further, these effects on γ oscillatory activity were found to be specific to the frontal brain region and were absent in the parieto-occipital brain region. CONCLUSIONS AND SIGNIFICANCE: We suggest that this opposing effect of rTMS on γ oscillatory activity in patients with SCZ versus healthy subjects may be related to homeostatic plasticity leading to differential effects of rTMS on γ oscillatory activity depending on baseline differences. These findings provide important insights into the neurophysiological mechanisms underlying WM deficits in SCZ and demonstrated that rTMS can modulate γ oscillatory activity that may be a possible avenue for cognitive potentiation in this disorder.
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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 | 0.000 |
| 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".