30.4 IDENTIFYING THE REDOX SUBTYPE OF SCHIZOPHRENIA USING ULTRA-HIGH FIELD 7T IMAGING
Bibliographic record
Abstract
Ultra-high field 7T imaging greatly enhances the estimation of altered structure-function correspondence in disease states and provides unprecedented access to estimate neurometabolites with high specificity. Glutathione and glutamate have emerged as promising therapeutic targets for patients showing inadequate response. Nevertheless, the role of these non-dopaminergic treatment targets in the neurobiology of treatment response is poorly understood. Using a longitudinal design and ultra-high field 7-Tesla magnetic resonance spectroscopy (MRS), we investigated the association of glutamate and glutathione with time to response in the dorsal anterior cingulate cortex (dACC) in patients with drug-naïve or minimally medicated first episode psychosis (n=26) and healthy controls (n= 27). Time to response was defined as the number of weeks it took to reach a 50% improvement on the PANSS-8 from baseline. We also studied the time-course of glutamate and glutathione changes using 7T functional spectroscopy and related metabolite dynamics at the onset to the early clinical course of psychosis. Higher dACC glutathione at baseline was associated with decreased time to response, while higher dACC glutamate was associated with functional impairment at baseline. There were no significant differences between patients and controls on measures of glutamate, or glutathione. Glutamate and glutathione levels are highly correlated in healthy controls, but this association is weakened among patients. In a sample of acutely psychotic drug-naïve first-episode subjects, the highly resolved dynamic spectroscopic measures emerge as promising markers of early prognostic course. We believe these results add credibility to the claim that an aberrant redox subtype of schizophrenia can be identified early on at the time of first episode psychosis.
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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 | 0.001 | 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) | 0.011 | 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".