Cortical Thinning in Network-Associated Regions in Cognitively Normal and Below-Normal Range Schizophrenia
Bibliographic record
Abstract
This study assessed whether cortical thickness across the brain and regionally in terms of the default mode, salience, and central executive networks differentiates schizophrenia patients and healthy controls with normal range or below-normal range cognitive performance. Cognitive normality was defined using the MATRICS Consensus Cognitive Battery (MCCB) composite score ( T=50 ± 10 ) and structural magnetic resonance imaging was used to generate cortical thickness data. Whole brain analysis revealed that cognitively normal range controls ( n=39 ) had greater cortical thickness than both cognitively normal ( n=17 ) and below-normal range ( n=49 ) patients. Cognitively normal controls also demonstrated greater thickness than patients in regions associated with the default mode and salience, but not central executive networks. No differences on any thickness measure were found between cognitively normal range and below-normal range controls ( n=24 ) or between cognitively normal and below-normal range patients. In addition, structural covariance between network regions was high and similar across subgroups. Positive and negative symptom severity did not correlate with thickness values. Cortical thinning across the brain and regionally in relation to the default and salience networks may index shared aspects of the psychotic psychopathology that defines schizophrenia with no relation to cognitive impairment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".