Correlations of cerebello-thalamo-prefrontal structure and neurological soft signs in patients with first-episode psychosis
Bibliographic record
Abstract
OBJECTIVE: This study aimed at determining brain structural imaging correlates of neurological soft signs (NSS) in patients suffering from a first-episode psychosis. METHOD: Fifty-two patients with a DSMIV diagnosis of first-episode psychosis (schizophrenia or schizophrenia spectrum disorder) were consecutively included. Subjects were assessed using a standardized neurological examination for motor coordination, motor integration and sensory integration. Anatomical magnetic resonance images (MRI) were analysed in the whole brain using optimized voxel-based morphometry. RESULTS: Neurological soft signs (NSS) total score (P-corrected = 0.013) and motor integration subscore (P-corrected = 0.035) were found to negatively correlate with grey matter structure of the dorsolateral prefrontal cortices. Motor coordination subscore was positively correlated with grey matter structure of the thalami (P-corrected = 0.002) and negatively with white matter structure of the cerebellum (P-corrected = 0.034). The addition of age and gender as covariate yielded similar results. We did not find any correlation between neither sensory integration subscore and grey matter structure nor NSS total score, motor integration subscore and voxel-based morphometry (VBM) white matter structure. CONCLUSION: Structural alteration in the cerebello-thalamo-prefrontal network is associated with neurological soft signs in schizophrenia, a candidate network for 'cognitive dysmetria'.
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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) | 0.003 | 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".