Cognitive Deficits and Clinical Symptoms with Hippocampal Subfields in First-Episode and Never-Treated Patients with Schizophrenia
Bibliographic record
Abstract
Memory dysfunction and associated hippocampal disturbances play crucial roles in cognitive impairment of schizophrenia. To examine the relationships between cognitive function and the hippocampal subfields (HSs) in first-episode never-treated (FENT) schizophrenia patients, the HSs were segmented in 39 FENT patients and 30 healthy controls using a state-of the-art automated algorithm. We found no significant differences in any HSs between the patients and controls. However, multivariate regression analysis showed that the left cornu ammonis 1 (CA1), left hippocampal tail, left presubiculum, and right molecular layer contributed 40% to the variance of the PANSS negative symptom score. After adjusting for sex, age, education, and intracranial volume, the partial correlation analysis showed that the volumes of left CA1, CA3, CA4, molecular layer, granule cell layer and both left and right subiculum were negatively correlated with the MATRICS consensus cognitive battery (MCCB) Hopkins Verbal Learning Test (HVLT). Multiple regression analysis showed that the left CA1 and CA3 hippocampal abnormalities contributed 66% to the variance of the HVLT. Our results suggest no detectable HS deficits were found in FENT schizophrenia patients. However, the HSs may be involved in the symptoms and cognitive deficits of schizophrenia patients in the early phase of their illness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".