The neural substrate of gait and executive function relationship in elderly women: A PET study
Bibliographic record
Abstract
AIM: Understanding the relationship between age-related gait impairment, such as slow gait, and executive functioning in seniors may help identify individuals at higher risk of mobility decline, falls, and progression to dementia at earlier stages. We aim to identify brain regions concomitantly associated with poor gait and executive functioning in a cohort of well-functioning elderly women. METHODS: In total, 149 well-functioning women aged 70.1 ± 6.2 years underwent FDG-PET to evaluate regional cerebral metabolic rates of glucose normalized in reference to cerebellar glucose metabolic value (normalized-rCMRglc) in 16 brain areas. We assessed gait speed, step length and cadence under usual and fast conditions. Executive function was assessed using Trail-Making-Tests (TMT) A and B. RESULTS: Adjusted multiple regression analyses for potential covariates showed that TMT-B and ΔTMT (TMT B-A) were associated with gait speed and cadence at fast condition. Lower normalized-rCMRglc in the posterior cingulate and primary sensorimotor cortices were associated with longer TMT-B and ΔTMT times (i.e., lower executive function) as well as with slower gait speed and lower cadence at fast condition. Slower gait speed and lower cadence at fast condition were also associated with lower normalized-rCMRglc in the occipital and parietal cortices. There were no other significant associations. CONCLUSIONS: In healthy elderly women without impending disability or cognitive impairment, reduced glucose metabolism in the posterior cingulate and primary sensorimotor cortices were associated with both lower gait performance and executive functioning. Our results suggest that gait control and executive functions might share the same neural substrate. Geriatr Gerontol Int 2017; 17: 1873-1880.
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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 | 0.000 | 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".