The arcuate fasciculus network and verbal deficits in psychosis
Bibliographic record
Abstract
BACKGROUND: Verbal learning (VL) and fluency (VF) are prominent cognitive deficits in psychosis, of which the precise neuroanatomical contributions are not fully understood. We investigated the arcuate fasciculus (AF) and its associated cortical regions to identify structural abnormalities contributing to these verbal impairments in early stages of psychotic illness. METHODS: Twenty-six individuals with recent-onset psychosis and 27 healthy controls underwent cognitive testing (MATRICS Consensus Cognitive Battery) and structural/diffusion-weighted MRI. Bilaterally, AF anisotropy and cortical thickness, surface area and volume of seven cortical regions were investigated in relation to VL and VF performance in both groups. RESULTS: Reduced right superior temporal gyrus surface area and volume related to better VF in controls. In psychosis, greater right pars opercularis volume and reduced left lateralization of this region related to better VL, while greater right long AF fractional anisotropy and right pars orbitalis volume related to better VF, these findings not present in controls. Psychosis had reduced right pars orbitalis thickness compared to controls. CONCLUSION: Anatomical substrates for normal processing of VL and VF appear altered in recent-onset psychosis. A possible aberrant role of the right hemisphere arcuate fasciculus and fronto-temporal cortical regions in psychosis may contribute to deficits in VL and VF.
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 | 0.001 |
| 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".