White matter abnormalities in 22q11.2 deletion syndrome patients showing cognitive decline
Bibliographic record
Abstract
BACKGROUND: Decline in cognitive functioning precedes the first psychotic episode in the course of schizophrenia and is considered a hallmark symptom of the disorder. Given the low incidence of schizophrenia, it remains a challenge to investigate whether cognitive decline coincides with disease-related changes in brain structure, such as white matter abnormalities. The 22q11.2 deletion syndrome (22q11DS) is an appealing model in this context, as 25% of patients develop psychosis. Furthermore, we recently showed that cognitive decline also precedes the onset of psychosis in individuals with 22q11DS. Here, we investigate whether the early cognitive decline in patients with 22q11DS is associated with alterations in white matter microstructure. METHODS: We compared the fractional anisotropy (FA) of white matter in 22q11DS patients with cognitive decline [n = 16; -18.34 (15.8) VIQ percentile points over 6.80 (2.39) years] to 22q11DS patients without cognitive decline [n = 18; 17.71 (20.17) VIQ percentile points over 5.27 (2.03) years] by applying an atlas-based approach to diffusion-weighted imaging data. RESULTS: FA was significantly increased (p < 0.05, FDR) in 22q11DS patients with a cognitive decline in the bilateral superior longitudinal fasciculus, the bilateral cingulum bundle, all subcomponents of the left internal capsule and the left superior frontal-occipital fasciculus as compared with 22q11DS patients without cognitive decline. CONCLUSIONS: Within 22q11DS, the early cognitive decline is associated with microstructural differences in white matter. At the mean age of 17.8 years, these changes are reflected in increased FA in several tracts. We hypothesize that similar brain alterations associated with cognitive decline take place early in the trajectory of schizophrenia.
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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) | 0.001 | 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".