Impaired insight in schizophrenia is associated with higher frontoparietal cerebral blood flow: an arterial spin labeling study
Bibliographic record
Abstract
Impaired insight into illness occurs in up to 98% of patients with schizophrenia, depending on the stage of illness, and leads to negative clinical outcomes. Previous neuroimaging studies suggest that impaired insight in patients with schizophrenia may be related to structural and functional anomalies in frontoparietal brain regions. To date, limited studies have investigated the association between regional cerebral blood flow (CBF) and impaired insight in schizophrenia. Therefore, we sought to investigate the relationship between regional CBF, as measured by arterial spin labeling (ASL), and impaired insight in participants with schizophrenia. A total of 32 participants were included in the analysis. Impaired insight in patients with schizophrenia was measured using the VAGUS, Self-report (VAGUS-SR). Resting-state regional CBF was measured using pseudo-continuous ASL (pCASL) and extracted using SPM12 and REX toolbox. Whole brain analysis found that impaired insight was associated with higher regional CBF in the right angular gyrus, left supramarginal gyrus, and right superior frontal region when controlling for age, gender, smoking status, and illness severity. The results indicate that impaired insight in schizophrenia is related to regional CBF in frontoparietal areas. These neuroimaging findings can serve as therapeutic targets for intervention, such as with non-invasive brain stimulation.
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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) | 0.002 | 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".