Cognitive insight is associated with cortical thickness in first-episode psychosis
Bibliographic record
Abstract
Compared to non-clinical subjects, people with psychosis show poor cognitive insight as reflected in low Self-Reflectiveness and high Self-Certainty. Neuroimaging studies have reported that 1) low Self-Reflectiveness is associated with volumetric reductions in ventrolateral prefrontal cortex (VLPFC), 2) higher Self-Certainty is associated with volumetric reductions in hippocampus, and 3) higher Self-Certainty is associated with fractional anisotropy in the fornix, in people with psychosis. The aims of the current study were to expand on this research by 1) performing an exploratory whole-brain cortical thickness analysis of the neural correlates of cognitive insight, to reveal whether regions outside the VLPFC are important for cognitive insight, and 2) to evaluate associations between cognitive insight and subfields of the hippocampus, which are distinct, interacting, and have different functions. We also aimed to replicate previous research documenting associations between cognitive insight and 3) total hippocampal volumes and 4) fornix fractional anisotropy. Fifteen people with a first-episode psychosis completed the Beck Cognitive Insight Scale and provided magnetic resonance and diffusion tensor imaging scans. Cortical thickness and hippocampal volumes were analyzed in FreeSurfer, and fornixfractional anisotropy was analyzed in Diffusion Toolkit/TrackVis. Higher Self-Reflectiveness and lower Self-Certainty significantly associated with thickness and thinness in VLPFC, respectively, as well as thickness and thinness in widespread frontal, parietal and temporal cortices. No associations emerged between Self-Reflectiveness or Self-Certainty and hippocampal total or sub-field volumes, or fornix fractional anisotropy. Results suggest that the neural correlates of cognitive insight involve a network of frontal, temporal and parietal brain regions.
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 | 0.002 |
| 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".