Gait Variability Is Associated With Gray Matter Volumes Implicated in Cognitive Function: A Cross-Sectional Analysis From the AGUEDA Trial
Bibliographic record
Abstract
Abstract Background and Objectives Aging is associated with both gait impairments and cognitive decline; however, the relationship between specific gait variability parameters, gray matter volume (GMV), and cognitive function remains poorly understood. This study aims to examine the associations between gait variability parameters (derived from stride length, step length, step time, and gait velocity) and GMV and its associations with cognitive function in cognitively normal older adults. Research Design and Methods Eighty-seven older adults (48 female) aged 65–80 from the AGUEDA trial participated in this cross-sectional analysis. The Optogait system was used to record gait parameters. T1-weighted brain images were acquired magnetic resonance imaging scanner, and GMV was calculated by whole-brain voxel-based morphometric analysis using SPM12. Cognitive function was calculated from different cognitive tests. Results Greater stride length variability was associated with lower GMV (p < .001) in clusters located in the supramarginal gyrus (t = 4.014, k = 179, β = -0.494) and hippocampus (t = 3.670, k = 334, β = -0.394), whereas greater step length variability was linked to lower GMV in the parahippocampal gyrus (t = 3.624, k = 76, β = -0.410). However, greater step time variability was associated with greater GMV in the supplementary motor area (t = 4.117, k = 274, β = 0.449). Gait velocity variability did not show any association with GMV. Furthermore, greater GMV in the supramarginal gyrus was associated with better working memory (β = 0.252, p = .008); greater GMV in the hippocampus was associated with better attentional/inhibitory control (β = 0.275, p = .010); and greater GMV in the parahippocampal gyrus was associated with better EF (β = 0.212, p = .035), attentional/inhibitory control (β = 0.241, p = .019), and working memory (β = 0.233, p = .027). Discussion and Implications These results suggest that gait variability could be an indicator of neurocognitive decline in older adults. Understanding these associations is essential for early dementia detection and sheds light on the complex interplay between physical function, brain health, and cognitive function during aging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".