Hippocampal Subfield Volumes in First Episode and Chronic Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Reduced hippocampal volume in schizophrenia is a well-replicated finding. New imaging techniques allow delineation of hippocampal subfield volumes. Studies including predominantly chronic patients demonstrate differences between subfields in sensitivity to illness, and in associations with clinical features. We carried out a cross-sectional and longitudinal study of first episode, sub-chronic, and chronic patients, using an imaging strategy that allows for the assessment of multiple hippocampal subfields. METHODS: Hippocampal subfield volumes were measured in 34 patients with schizophrenia (19 first episode, 6 sub-chronic, 9 chronic) and 15 healthy comparison participants. A subset of 10 first episode and 12 healthy participants were rescanned after six months. RESULTS: Total left hippocampal volume was smaller in sub-chronic (p = 0.04, effect size 1.12) and chronic (p = 0.009, effect size 1.42) patients compared with healthy volunteers. The CA2-3 subfield volume of chronic patients was significantly decreased (p = 0.009, effect size 1.42) compared to healthy volunteers. The CA4-DG volume was significantly reduced in all three patient groups compared to healthy group (all p < 0.005). The two affected subfield volumes were inversely correlated with severity of negative symptoms (p < 0.05). There was a small, but statistically significant decline in left CA4-DG volume over the first six months of illness (p = 0.01). CONCLUSIONS: Imaging strategies defining the subfields of the hippocampus may be informative in linking symptoms and structural abnormalities, and in understanding more about progression during the early phases of illness in 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".