White matter microstructure alterations in systemic lupus erythematosus: A preliminary coordinate-based meta-analysis of diffusion tensor imaging studies
Bibliographic record
Abstract
BACKGROUND: Systemic lupus erythematosus is often accompanied with neuropsychiatric symptoms. Neuroimaging evidence indicated that microstructural white matter (WM) abnormalities play role in the neuropathological mechanism. Diffusion tensor imaging (DTI) studies allows the assessment of the microstructural integrity of WM tracts, but existing findings were inconsistent. This present study aimed to conduct a coordinate-based meta-analysis (CBMA) to identify statistical consensus of DTI studies in SLE. METHODS: Relevant studies that reported the differences of fractional anisotropy (FA) between SLE patients and healthy controls (HC) were searched systematically. Only studies reported the results in Talairach or Montreal Neurological Institute (MNI) coordinates were included. The anisotropic effect size version of signed differential mapping (AES-SDM) was applied to detect WM alterations in SLE. RESULTS: Totally, five studies with seven datasets which included 126 patients and 161 HC were identified. The pooled meta-analysis demonstrated that SLE patients exhibited significant FA reduction in the left striatum and bilateral inferior network, mainly comprised the corpus callosum (CC), bilateral inferior fronto-occipital fasciculus (IFOF), bilateral anterior thalamic projections, bilateral superior longitudinal fasciculus (SLF), left inferior longitudinal fasciculus (ILF), and left insula. No region with higher FA was identified. CONCLUSIONS: Disorders of the immune system might lead to subtle WM microstructural alterations in SLE, which might be related with cognitive deficits or emotional distress symptoms. This provides a better understanding of the pathological mechanism of microstructural brain abnormalities in SLE.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".