Structural Neural Correlates of Memory Performance in Schizoprhenia as Revealed by Cortical Thickness
Bibliographic record
Abstract
In addition to the positive and negative symptoms, schizophrenia is associated with a variety of cognitive impairments, and in particular with episodic memory deficits. Functional neuroimaging studies have begun exploring the potential neural correlates of memory deficits but there are few reports of structural brain abnormalities underlying memory impairment in schizophrenia. We investigated the potential association between morphological brain abnormalities as revealed by cortical thickness measures and episodic memory performance on a face recognition task. Differences in regional cortical thickness between 27 patients with a DSM-IV diagnosis of schizophrenia and 28 control matched subjects were investigated using MRI T1 images and computer image analysis (CIVET pipeline; Lerch and Evans, 2005). Cortical thickness was estimated as the shortest distance between the pial surface of the cerebral cortex and the white-matter/gray-matter interface surface at numerous points (40 962 vertices) across the cortical mantle. Consistent with previous studies, a group comparison revealed thinner cortex in the patient group relative to controls in the right prefrontal cortex and parahippocampal gyrus. Interestingly, a significant positive correlation between memory performance and cortical thickness of the anterior cingulate, bilaterally as well as the right parahippocampal gyrus was noted in the schizophrenia group. That is, the thinner the cortex in those regions, the more impaired the patients were in terms of memory performance as compared to healthy participants.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".