Glutamatergic deficit and schizophrenia-like negative symptoms: new evidence from ketamine-induced mismatch negativity alterations in healthy male humans
Bibliographic record
Abstract
BACKGROUND: -methyl-D-aspartate receptor (NMDAR) is a major translational approach for treating negative symptoms of schizophrenia. Ketamine comprehensively produces schizophrenia-like symptoms, such as positive, cognitive and negative symptoms in healthy volunteers. The amplitude of the mismatch negativity (MMN) is known to be significantly reduced not only in patients with schizophrenia, but also in healthy controls receiving ketamine. Accordingly, it was the aim of the present study to investigate whether changes of MMN amplitudes during ketamine administration are associated with the emergence of schizophrenia-like negative symptoms in healthy volunteers. METHODS: We examined the impact of ketamine during an MMN paradigm with 64-channel electroencephalography (EEG) and assessed the psychopathological status using the Positive and Negative Syndrome Scale (PANSS) in healthy male volunteers using a single-blind, randomized, placebo-controlled crossover design. Low-resolution brain electromagnetic tomography was used for source localization. RESULTS: Twenty-four men were included in our analysis. Significant reductions of MMN amplitudes and an increase in all PANSS scores were identified under the ketamine condition. Smaller MMN amplitudes were specifically associated with more pronounced negative symptoms. Source analysis of MMN generators indicated a significantly reduced current source density (CSD) under the ketamine condition in the primary auditory cortex, the posterior cingulate and the middle frontal gyrus. LIMITATIONS: The sample included only men within a tight age range of 20-32 years. CONCLUSION: The MMN might represent a biomarker for negative symptoms in schizophrenia related to an insufficient NMDAR system and could be used to identify patients with schizophrenia with negative symptoms due to NMDAR dysfunction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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".