Entorhinal Cortex Volume Is Associated With Dual-Task Gait Cost Among Older Adults With MCI: Results From the Gait and Brain Study
Bibliographic record
Abstract
BACKGROUND: Low dual-task gait performance (the slowing of gait speed while performing a demanding cognitive task) is associated with low cognitive performance and an increased risk of progression to dementia in older adults with mild cognitive impairment. However, the reason for this remains unclear. This study aimed to examine the relationship between dual-task cost and regional brain volume, focusing on the hippocampus, parahippocampal gyrus, entorhinal cortex, and motor and lateral frontal cortices in older adults with mild cognitive impairment. METHODS: Forty older adults with mild cognitive impairment from the "Gait and Brain Study" were included in this study. Gait velocity was measured during single-task (ie, walking alone) and dual-task (ie, counting backwards, subtracting serial sevens, and naming animals, in addition to walking) conditions, using an electronic walkway. Regional brain volumes were derived by automated segmentation, using 3T magnetic resonance imaging. RESULTS: Partial rank correlation analyses demonstrated that a smaller volume of the left entorhinal cortex was associated with higher dual-task costs in counting backwards and subtracting serial sevens conditions. Subsequent logistic regression analyses demonstrated that a smaller volume of the left entorhinal cortex was independently associated with higher dual-task cost (slowing down >20% when performing cognitive task) in these two conditions. There were no other significant associations. CONCLUSIONS: Our results show that lower dual-task gait performance is associated with volume reduction in the entorhinal cortex. Cognitive and motor dysfunction in older adults with mild cognitive impairment may reflect a shared pathogenic mechanism, and dual-task-related gait changes might be a surrogate motor marker for Alzheimer's disease pathology.
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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 | 0.000 |
| 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".