Physical reserve and its underpinning functional neural networks moderate the relationship between white matter hyperintensity and postural balance in older adults with subcortical ischemic vascular cognitive impairment
Bibliographic record
Abstract
White matter hyperintensities (WMH) are markers of subcortical ischemic vascular cognitive impairment (SIVCI) associated with impaired postural balance. Physical reserve (PR) is a recently established construct that reflects one's capacity to maintain physical function despite brain pathology. This cross-sectional study aims to map functional networks associated with PR, and examining the relationship between PR, WMH, and postural balance. PR was defined in 22 community-dwelling older adults with SIVCI. Functional networks of PR were computed using general linear model. Subsequent analyses examined whether PR and relevant networks moderated the relationship between WMH and postural balance under two conditions-eyes open while standing on foam (EOF) or on floor (EONF). We found that PR and the relevant networks-frontoparietal network (FPN) and default mode network (DMN)-significantly moderated the association between WMH and postural balance. For individuals with high PR, postural balance remained stable regardless of the extent of WMH load; whereas for those with low PR, postural balance worsened as WMH load increased. These results suggest the attenuated effects of WMH on postural stability due to PR may be underpinned by functional neural network reorganization in the FPN and DMN as a part of compensatory processes.
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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 | 0.002 |
| 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".