Myelin integrity in older adults with vascular cognitive impairment: Implications for mobility performance
Bibliographic record
Abstract
Abstract Background In individuals prone to white matter pathology, such as older adults with vascular cognitive impairment (VCI), myelin loss could contribute to mobility impairment. Using myelin water fraction (MWF), an in‐vivo technique to quantify myelin content in the brain, we investigated whether myelin content is associated with mobility performance in older adults with VCI. Method We analyzed cross‐sectional data from thirty‐two subjects with MRI‐evidence of subcortical ischemic VCI who underwent 3T MRI scanning protocol at the UBC MRI Research Centre (mean [SD], age = 73.8 [5.4], MoCA score = 21.3 [3.8], 65.8% female). The protocol consisted of a standard T1‐weighted contrast for acquisition of high‐resolution structural data, and a gradient and spin echo contrast (GRASE) for acquisition of MWF data. Gait speed (meters/second) collected via a 4‐meter walking test was used as a measure of mobility performance. Following a preprocessing pipeline using the FMRIB Software Library, MWF data were computed for the whole‐brain white matter, and fifteen specific white matter structures of interest. The T1‐weighted images were processed with FreeSurfer to determine estimated intracranial volume (eICV). Hierarchical regression models adjusting for eICV and MoCA were conducted to determine whether lower MWF is associated with slower gait speed. Result In models adjusting for eICV only, lower MWF in the superior longitudinal fasciculus was associated with slower gait speed (unstandardized B [95% CI] = 3.287 [0.052 to 6.523], R2 Change = 0.130, p = 0.047). Adjusting for MoCA scores attenuated this relationship (unstandardized B = 3.055 [‐0.155 to 6.265], R2 Change = 0.111, p = 0.061). Conclusion To our knowledge, this is the first study to investigate whether in‐vivo lower myelin content is linked to poor mobility in this population. Our findings suggest that poor myelin integrity in specific white matter tracts of the brain may be linked to decline in mobility performance in older adults with VCI. Understanding the mechanisms by which VCI leads to mobility impairment is the first step towards developing strategies to prevent downstream unwanted health outcomes such as falls, hospitalizations, and loss of independence.
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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 | 0.003 |
| 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 | 0.001 | 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".