Alterations of White Matter Integrity in Cerebral Small Vessel Disease and Their Correlation with Cognitive Performance: A Trace-Based Spatial Statistics Study
Bibliographic record
Abstract
Abstract Background: This study aimed to understand the injury of white matter (WM) microstructure behind white matter hyperintensities (WMH) and identify the regions where injury was more pronounced with increasing WMH severity. Moreover, we analyzed whether this microstructural injury is related to cognition. Methods: 110 patients with WMH were recruited in this research. All subjects underwent 3.0T MRI scans and neuropsychological cognitive assessments. Simple mental state examination (MMSE) along with Montreal Cognitive Assessment (MoCA) were applied to assess the patient's overall cognitive ability. WMH of each subject was graded according to Fazekas grade scale and was divided into two groups: (A) WMH score of 1-2 points (n=64), (b) WMH score of 3-6 points (n=46). Trace-based spatial statistics (TBSS) was applied for the analysis of diffusion tensor imaging (DTI) data. All statistical analyses were performed in SPSS 26.0 statistical software. Results: The results indicate that patients with higher WMH scores showed extensively symmetrical areas of increased mean diffusion, axial diffusion and radial diffusion involving bilateral anterior limb, posterior limb and retrolenticular part of internal capsule, posterior corona radiata, external capsule, superior longitudinal fasciculus, and superior fronto-occipital fasciculus (P < 0.01). Conclusions: Finally, we come to the conclusion that cognition-related WM fiber tracts tend to be more vulnerable to be injured in patients of cerebral small vessel disease (CSVD). Moreover, changes in WM microstructure often predate changes of cognition. Early detection of microstructural changes and timely intervention can delay cognitive decline to some extent.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".