The Role of Gamma Oscillations in the Pathophysiology of Substance Use Disorders
Bibliographic record
Abstract
Substance use disorders (SUDs) are a major public health problem-with over 200 million people reporting drug use in 2016. Electroencephalography (EEG) is a powerful tool that can provide insights into the impact of SUDs on cognition. Specifically, modulated gamma activity may provide an index of the pathophysiology of SUDs. Thus, the purpose of this review was to investigate the impact of alcohol, tobacco, cannabis, cocaine, and amphetamine on gamma activity, among pre-clinical and clinical populations during acute and chronic exposure and withdrawal states. We searched multiple databases for key terms related to SUDs, EEG, and gamma and ensured rigorous methods by using a standardized review reporting tool. We included 30 studies in this review and found that all substances were associated with modulation of gamma activity, across states and in both preclinical and clinical populations. Gamma oscillations appeared to be differentially modulated in clinical versus preclinical populations and had the most complex relationship with alcohol, indicating that it may act differently than other substances. The findings of this review offer insights into the pathophysiology of SUDs, providing a potential window into novel treatments for SUDs via modulation of gamma activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".