Dynamic cerebral reorganization in the pathophysiology of schizophrenia: a MRI-derived cortical thickness study
Bibliographic record
Abstract
BACKGROUND: A structural neuroanatomical change indicating a reduction in brain tissue is a notable feature of schizophrenia. Several pathophysiological processes such as aberrant cortical maturation, progressive tissue loss and compensatory tissue increase could contribute to the structural changes seen in schizophrenia. METHOD: We studied cortical thickness using surface-based morphometry in 98 clinically stable patients with schizophrenia and 83 controls. Using a pattern classification approach, we studied whether the features that discriminate patients from controls vary across the different stages of the illness. Using a covariance analysis, we also investigated if concurrent increases accompany decreases in cortical thickness. RESULTS: Very high levels of accuracy (96.3%), specificity (98.8%) and sensitivity (88%) were noted when classifying patients with <2 years of illness from controls. Within the patient group, reduced thickness was consistently accompanied by increased thickness in distributed brain regions. A pattern of cortical amelioration or normalization (i.e. reduced deviation from controls) was noted with increasing illness duration. While temporo-limbic and fronto-parietal regions showed reduced thickness, the occipital cortex showed increased thickness, especially in those with a long-standing illness. CONCLUSION: A compensatory remodelling process might contribute to the cortical thickness variations in different stages of schizophrenia. Subtle cerebral reorganization reflecting the inherent plasticity of brain may occur concomitantly with processes contributing to tissue reduction in adult patients with schizophrenia.
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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) | 0.001 | 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".