Verbal memory impairments in schizophrenia associated with cortical thinning
Bibliographic record
Abstract
Verbal memory (VM) represents one of the most affected cognitive domains in schizophrenia. Multiple studies have shown that schizophrenia is associated with cortical abnormalities, but it remains unclear whether these are related to VM impairments. Considering the vast literature demonstrating the role of the frontal cortex, the parahippocampal cortex, and the hippocampus in VM, we examined the cortical thickness/volume of these regions. We used a categorical approach whereby 27 schizophrenia patients with 'moderate to severe' VM impairments were compared to 23 patients with 'low to mild' VM impairments and 23 healthy controls. A series of between-group vertex-wise GLM on cortical thickness were performed for specific regions of interest defining the parahippocampal gyrus and the frontal cortex. When compared to healthy controls, patients with 'moderate to severe' VM impairments revealed significantly thinner cortex in the left frontal lobe, and the parahippocampal gyri. When compared to patients with 'low to mild' VM impairments, patients with 'moderate to severe' VM impairments showed a trend of thinner cortex in similar regions. Virtually no differences were observed in the frontal area of patients with 'low to mild' VM impairments relative to controls. No significant group differences were observed in the hippocampus. Our results indicate that patients with greater VM impairments demonstrate significant cortical thinning in regions known to be important in VM performance. Treating VM deficits in schizophrenia could have a positive effect on the brain; thus, subgroups of patients with more severe VM deficits should be a prioritized target in the development of new cognitive treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".