Increased grey matter densities in schizophrenia patients with negative symptoms after treatment with quetiapine: a voxel-based morphometry study
Bibliographic record
Abstract
Among new-generation antipsychotics, quetiapine was found to be associated with a partial 'normalization' of reduced functional activation in prefrontal and temporal areas and studies conducted by our group found a clinical improvement in negative symptoms in addition to restoration of frontal activation in schizophrenia patients with blunted affect after treatment with quetiapine. Here we investigated the parallelism between improved clinical symptoms and grey mater density (GMD) changes in the frontal region after quetiapine treatment in 15 schizophrenia patients. We hypothesize that improvement in clinical symptoms will be associated with change in GMD in prefrontal regions of interest. By using voxel-based morphometry, paired t-test random-effect analysis showed a significant increase in GMD bilaterally in the inferior frontal cortex/orbitofrontal gyrus and anterior cingulate cortex after 5.5 months of treatment with quetiapine. This GMD increase was associated with a significant improvement in negative symptoms. When GMD was correlated with psychiatric assessment scores, there was a negative correlation between GMD in the anterior cingulate cortex and the Rating Scale for Emotional Blunting score (r=-665, P=0.008) and between the orbitofrontal gyrus and the total Positive and Negative Syndrome Scale negative score (r=-764, P=0.001). Results suggest that increased GMD in some frontal regions are associated with an improvement of negative symptoms. Although not unique to quetiapine, it would be reasonable to attribute the GMD changes in the study to treatment.
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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) | 0.001 | 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".