Mapping the neural substrate of high dual-task gait cost in older adults across the cognitive spectrum
Bibliographic record
Abstract
The dual task cost of gait (DTC) is an accessible and cost-effective test that can help identify individuals with cognitive decline and dementia. However, its neural substrate has not been widely described. This study aims to investigate the neural substrate of the high DTC in older adults across the spectrum of cognitive decline. A total of 336 individuals from the GAIT study cohort were analyzed, including cognitively healthy (N = 122, 71 ± 3.6 years), those with mild cognitive impairment (N = 168, 71 ± 5.3 years), and those with dementia (N = 46, 80 ± 5.7 years). A DTC of 20% or greater was considered to indicate a high level of slowing down while performing successively two verbal tasks (counting backwards task by ones and naming animals). Voxel-based morphometry was employed to investigate differences in gray matter volume (GMV) between groups, which were dichotomized according to the DTC. A high DTC in the whole population (N = 336) was associated with a smaller GMV in the bilateral temporal lobe across both dual-task conditions. A moderation analysis was employed to compare the neural substrate between cognitive status groups. This revealed that the dementia group exhibited an additional cluster located in the left precentral gyrus with GMV loss associated with a high naming animals DTC, in contrast to the other cognitive groups. These results provide new evidence on why dual-task gait capabilities deteriorate in normal and pathological cognitive aging. A more precise understanding of the neural substrate associated with high DTC and cognitive status would help elucidate its use in clinical and research settings.
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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 | 0.001 | 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".