White matter integrity and verbal memory following a first episode of psychosis: A longitudinal study
Bibliographic record
Abstract
Psychotic disorders are heterogeneous disorders for which there is evidence of structural and functional brain abnormalities. The role of white matter integrity, often measured via Fractional Anisotropy (FA), has played a controversial role in individuals with a first episode of psychosis (FEP). Similarly, some FEP studies have observed that higher FA is associated with better verbal memory, but others failed to find such an association. Studying the early stages of psychosis represents a promising avenue to overcome previous confounding factors and characterize the disease in its early clinical stages. Eighty individuals with a FEP were recruited from a specialized early intervention program for psychosis alongside 55 non-clinical controls from the community matched for age and sex. Both groups were followed and scanned 4 times: at baseline (within 3 months after program entry), 6 months, 12 months, and 18 months. Tract-Based Spatial Statistics (TBSS) were used on 3.0 Tesla diffusion-weighted images to extract fractional anisotropy values for white matter regions of interest in accordance with the John Hopkins University white-matter tractography atlas. The analysis revealed no significant main effect of group or time, and no significant associations between FA and verbal memory. Overall, differences in FA are small early in the course of illness and longer follow-up periods may be required to identify possible changes during a critical intervention window. • Fractional anisotropy (FA) has a questionable role in first episode psychosis (FEP). • In largest longitudinal FEP sample to date, no FA changes or group difference found. • No significant associations between FA and verbal memory in FEP. • FA is still within normal range 18 months following FEP onset. • FEP represents a critical window for early intervention.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".