Cortical Network Disruption is Minimal in Early Stages of Psychosis
Bibliographic record
Abstract
1 Abstract Background and Hypothesis Chronic schizophrenia is associated with white matter disruption and topological reorganization of cortical connectivity but the trajectory of these changes over the disease course are poorly understood. Current white matter studies in first-episode psychosis (FEP) patients using diffusion magnetic resonance imaging (dMRI) suggest such disruption may be detectable at the onset of psychosis, but specific results vary widely and few reports have contextualized their findings with direct comparison to chronic patients. Here, we test the hypothesis that structural changes are not a significant feature of early psychosis. Study Design Diffusion and T1-weighted 7T MR scans were obtained from N=113 (61 FEP patients, 37 controls, 15 chronic patients) recruited from an established cohort in London, Ontario. Voxel- and network-based analyses were used to detect changes in diffusion microstructural parameters. Graph theory metrics were used to probe changes in the cortical network hierarchy and to assess the vulnerability of hub regions to disruption. Experiments were replicated with N=167 (111 patients, 56 controls) from the Human Connectome Project - Early Psychosis (HCP-EP) dataset. Study Results Widespread microstructural changes were found in chronic patients, but changes in FEP patients were minimal. Unlike chronic patients, no appreciable topological changes in the cortical network were observed in FEP patients. These results were replicated in the early psychosis patients of the HCP-EP datasets, which were indistinguishable from controls on nearly all metrics. Conclusions The white matter structural changes observed in chronic schizophrenia are not a prominent feature in the early stages of this illness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".