Functional Neural Correlates of Slower Gait Among Older Adults With Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Subtle, but observable, changes in mobility often exist among older adults with mild cognitive impairment (MCI). Notably, these changes are not inconsequential. Therefore, there is a strong interest to better understand the underlying neural correlates of gait slowing among older adults with MCI. In this study, we aimed to characterize patterns of functional connectivity associated with slower gait speed in older adults with MCI. METHODS: Forty-nine participants aged 60 years and older with MCI were included in the cross-sectional study. All participants underwent assessments of gait speed and resting state functional magnetic resonance imaging. RESULTS: In this sample of older adults with MCI, slower usual gait was characterized by altered connectivity between the sensorimotor network (SMN) and the frontoparietal network (FPN) (p < .05)-specifically, slower usual gait was associated with greater connectivity between the supplementary motor area (SMA) and the bilateral ventral visual cortices (p = .01); lower connectivity between the SMA and the bilateral superior lateral occipital cortex (p < .01); and lower connectivity between the SMA and the bilateral frontal eye field (p < .01). CONCLUSION: Altered inter-network functional connectivity between the SMN and FPN may be a neural mechanism for slowing of gait in older adults with MCI.
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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".