4.2 Striatal Glutamate as Biomarker of Clinical Response to First-Line Treatment in Antipsychotic-naïve, First-Episode Psychosis Patients
Bibliographic record
Abstract
Background: Glutamate level in the precommissural dorsal caudate (i.e., associative striatum) is increased in antipsychotic-naive patients with first-episode psychosis (FEP). This increase normalizes after effective clinical response to first-line antipsychotic treatment. However, it is unclear if glutamate level is different between FEP resistant and responsive to first-line treatment and if glutamate could assist as a biomarker to predict treatment response. Methods: Forty-eight antipsychotic-naive FEP patients were studied. Patients were treated open label with risperidone, with doses adjusted based on clinical judgment. Response was defined as a reduction of 40% or more in the PANSS positive symptom subscale after 4 weeks of treatment. 1H-MRS scans were obtained from the right precomissural dorsal-caudate (TE = 35 ms) at baseline prior to start of treatment and after 4 weeks of treatment. Glutamate levels were estimated using LC Model and corrected for the cerebrospinal fluid proportion within the voxel. Results: After 4 weeks of treatment, 29 patients responded to treatment and 19 did not. At the baseline scan, patients assigned as resistant showed trend-level glutamate elevations compared to responders (P = .07). In the 4-week scan, resistant patients showed higher levels of glutamate compared to responders (P = .02). Conclusion: Our results support that striatal glutamate is higher in FEP patients that are resistant to first-line treatment in comparison to responsive patients. In addition, our results suggest that baseline striatal glutamate level could predict treatment response. These results should be confirmed with a larger sample, different first-line antipsychotics, and during a second treatment trial of first-line antipsychotic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".