T163. Submission Withdrawn
Bibliographic record
Abstract
Poster Session I S179early neurodevelopmental pathologies.It is unclear, however, whether such structural changes might be evident across the schizophrenia spectrum, involving at-risk subjects as well as even healthy subjects with subclinical or attenuated psychotic(-like) symptoms Methods: We analysed high-resolution MRI scans (3 Tesla, T1-weighted MPRAGE, 1x1x1mm resolution) from n=177 healthy subjects with no current or previous psychiatric condition recruited from the local community.Subjects completed the SCL90R, a general symptom checklist (i.e.self-rating of symptoms), which includes subscales for psychoticism (with subclinical psychotic/-like symptoms) and paranoid ideation.We used the CAT12 toolbox to analyse both gyrification using the absolute mean curvature approach (Luders et al., NeuroImage 2006, as well as cortical thickness and voxel-based morphometry (VBM).Results: Correcting for effects of age and gender, we found a significant negative correlation between SCL90R psychoticism scores and gyrification in a left prefrontal / frontopolar cluster, but no similar finding for wither cortical thickness analysis nor analyses of the paranoid ideation subscale.Discussion: Our results suggest that prefrontal gyrification might be a marker for psychotic phenotypes spanning a spectrum from subclinical symptom expression to frank psychosis.This association seems linked to gyrification (rather than other markers of brain structure), which would suggest a relative specificity.Hence, this would be consistent with the assumption that gyrification is related to early neurodevelopmental effects, which lead to liabity to experiencing psychotic symptoms later in life, and might thus serve as an imaging phenotype for early risk detection and intervention in high-risk groups.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.745 | 0.503 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".