Surface-Based Morphometry Findings Reveal Structural Alterations of the Brain in Meige Syndrome
Bibliographic record
Abstract
OBJECTIVE: To compare structural alterations in the brains of Meige syndrome (MS) patients with those of healthy controls (HCs) by using surface-based morphometry (SBM) and compare structural differences between the brains of MS patients with sleep disorders and those of MS patients without sleep disorders. METHODS: We investigated cortical surface parameters in 42 MS patients and 30 HCs. T1-weighted images were acquired and processed using CAT12 to perform vertexwise between-group comparisons of cortical thickness, gyrification, cortical complexity and sulcus depth with validated quality control protocols. We also performed SBM to analyze data from 19 patients with sleep disorders and 23 patients without sleep disorders. RESULTS: Compared with HCs, MS patients had differences in large clusters of cortical regions, especially in postcentral, precentral, superior frontal and paracentral thickness. Differences were also observed in the parietal and occipital areas. Among MS patients with and without sleep disorders, altered cortical complexity and sulcal depth were observed. CONCLUSIONS: This study strongly suggested that MS patients have cortical structural abnormalities compared with HCs, thus elucidating the underlying pathophysiology of motor and nonmotor symptoms in MS patients.
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 | 0.000 |
| 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".