Sex differences and symptom based gray and white matter densities in schizophrenia
Bibliographic record
Abstract
We investigated the association between densities in gray matter (GMD) and white matter (WMD) phenotypes and positive (PS) and negative (NS) symptoms in 40 schizophrenia patients (SZ). Cerebral densities were compared with 41 normal controls (NC) matched for age and sex using voxel-based morphometry on T1-3T-MRI. We found decreased GMD in the anterior cingulate-temporal gyri and increased GMD in the posterior cingulate gyrus in SZ relative to NC. WMD reduction was found in the inferior frontal and posterior parietal regions in SZ relative to NC. GMD in the insula/caudate correlated with PS, while GMD in the middle frontal gyrus and cerebellum correlated with NS. WMD in the middle frontal and superior frontal regions correlated with PS and NS respectively. Invers correlations were found between GMD in the parietal lobe and the uvula with PS. An inverse correlation was found between GMD in the cerebellum and NS. Inverse correlation was also found in the WMD of the occipital region and superior frontal regions with PS and NS respectively. Comparison between male groups revealed decreased total GMD in male patients, while no differences were observed between female groups. These correlational findings suggest that symptom profiles in schizophrenia show unique GM/WM phenotypes.
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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 | 0.000 | 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) | 0.002 | 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".