Lower myelin content is associated with poorer gait variability in older adults with cerebral small vessel disease and mild cognitive impairment
Bibliographic record
Abstract
Abstract Background Mobility decline is often observed in individuals with cerebral small vessel disease (cSVD). Loss of myelin in the cerebral white matter is a common feature of cSVD and could be one of the mechanisms contributing to poor mobility; however, this hypothesis remains to be tested. Using myelin water fraction (MWF), we investigated whether myelin content is associated with gait parameters in older adults with cSVD. Method Cross‐sectional data from individuals with cSVD and mild cognitive impairment were analyzed. Myelin content was assessed via MRI multi‐echo gradient and spin echo T2 relaxation sequence, indexed as myelin water fraction (MWF). Gait parameters were measured using an electronic walkway. Hierarchical regression models adjusting for total intracranial volume, age, sex, body mass index, and Mini‐Mental State Examination were conducted to determine the associations between MWF and gait parameters. Significant models were further adjusted for white matter hyperintensity burden. Result Sixty‐four participants were included (mean [SD], age = 75.2y [5.4], 62.5% female). In adjusted models, lower MWF in the cingulum (Unstandardized B (95% CI): ‐5.12 [‐9.21 to ‐1.04], R2change = 0.09, Fchange = 6.31, pchange = 0.015), superior longitudinal fasciculus (‐3.79 [‐7.29 to ‐0.29], R2change = 0.07, Fchange = 4.70, pchange = 0.034), posterior corona radiata (‐4.86 [‐9.45 to ‐0.27], R2change = 0.07, Fchange = 4.49, pchange = 0.039), and body of the corpus callosum (‐4.19 [‐8.19 to ‐0.20], R2change = 0.06, Fchange = 4.41, pchange = 0.040) was associated with higher cycle time variability. White matter hyperintensity burden weakened these associations. Conclusion In older adults with cSVD, Lower myelin content in specific white matter tracts may contribute to higher gait variability, increasing the overall risk of mobility impairment.
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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) | 0.002 | 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".