DTI study of white matter fiber integrity and the association with cognitive impairment after subcortical infarction
Bibliographic record
Abstract
Objective To study the relationship between white matter fiber integrity and cognitive impairment after subcortical infarction, and to explore the possible mechanism with diffusion tensor imaging (DTI). Methods Total of 37 subcortical infarction patients with cognitive impairment were recruitded from March 2016 to December 2018. Montreal Cognitive Assessment (MoCA) was used to evaluate the cognitive function. DTI was performed to obtain the fractional anisotropy (FA) of white matter fiber in the whole brain. The correlation between FA values and MoCA scores was analyzed by Pearson correlation analysis and partial correlation analysis and then the multiple linear regression model was established. Results Correlation analysis showed that decreased FA values in the bilateral external capsule, cingulate gyrus, and superior longitudinal fasciculus (SLF), inferior fronto⁃occipital fasciculus (IFOF), fasciculus，anterior limb of internal capsule, anterior and superior corona radiata were significantly correlated with MoCA scores (P < 0.05, for all). Multiple linear regression analysis showed that MoCA scores affected the SLF (P=0.042) and IFOF (P=0.006). Conclusions Impaired white matter filber integrity of the ipsilesional SLF and IFOF can be imaging biomarkers for cognitive impairment after subcortical infarction.
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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) | 0.000 | 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) | 0.001 | 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".