Using Mismatch Negativity to Investigate the Pathophysiology of Substance Use Disorders and Comorbid Psychosis
Bibliographic record
Abstract
Substance use disorders (SUDs) have a devastating impact on society and place a heavy burden on health care systems. Given that alcohol, tobacco, and cannabis use have the highest prevalence, further understanding of the underlying pathophysiology of these SUDs is crucial. Electroencephalography is an inexpensive, temporally superior, and translatable technique which enables investigation of the pathobiology of SUDs through the evaluation of various event-related potential components, including mismatch negativity (MMN). The goals of this review were to investigate the effects of acute and chronic alcohol, tobacco, and cannabis use on MMN among nonpsychiatric populations and patients with comorbid psychosis. A literature search was performed using the database PubMed, and 36 articles met our inclusion and exclusion criteria. We found a pattern of attenuation of MMN amplitude among patients with alcoholism across acute and chronic alcohol use, and this dysregulation was not heritable. Reports were limited, and results were mixed on the effects of acute and chronic tobacco and cannabis use on MMN. Reports on comorbid SUDs and psychosis were even fewer, and also presented mixed findings. These preliminary results suggest that MMN deficits may be associated with SUDs, specifically alcohol use disorder, and serve as a possible biomarker for treating these common disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".