T212. LEVELS OF GLUTAMATERGIC NEUROMETABOLITES IN PATIENTS WITH SEVERE TREATMENT-RESISTANT SCHIZOPHRENIA: A PROTON MAGNETIC RESONANCE SPECTROSCOPY STUDY
Bibliographic record
Abstract
Abstract Background Approximately 30% of patients with schizophrenia do not respond to antipsychotics and are thus considered to have treatment-resistant schizophrenia (TRS). To date, only four studies have examined glutamatergic neurometabolite levels using proton magnetic resonance spectroscopy (1H-MRS) in patients with TRS, collectively suggesting that glutamatergic dysfunction may be implicated in the pathophysiology of TRS. Notably, the TRS patient population in these studies had mild to moderate illness severity, which is not entirely reflective of what is observed in clinical practice. Methods In this present work, we compared glutamate+glutamine (Glx) levels in the dorsal anterior cingulate cortex (dACC) and caudate among patients with TRS, patients with non-TRS, and healthy controls (HCs), using 3T 1H-MRS (PRESS, TE=35ms). TRS criteria were defined by severe positive symptoms (i.e. ≥5 on 2 Positive and Negative Syndrome Scale (PANSS) positive symptom items or ≥4 on 3 PANSS positive symptom items) despite standard antipsychotic treatment. Results A total of 95 participants were included (29 TRS patients [PANSS=111.2±20.4], 33 non-TRS patients [PANSS=49.8±13.7], and 33 HCs). dACC Glx levels were higher in the TRS group versus HCs (group effect: F[2,75]=4.74, p=0.011; TRS vs. HCs: p=0.012). No group differences were identified in the caudate. There were no associations between Glx levels in these regions and clinical severity in either patient group. Discussion Our results confirm that higher dACC Glx might be related to the pathophysiology underlying TRS, by demonstrating this phenomenon in a TRS patient population reflective of clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".