ASSOCIATIONS BETWEEN FUNCTIONAL NETWORKS OF PHYSICAL RESERVE, POSTURAL INSTABILITY, AND WHITE MATTER LESIONS
Bibliographic record
Abstract
Abstract White matter hyperintensities (WMH) are clinical markers of subcortical ischemic vascular cognitive impairment (SIVCI) associated with impaired postural balance and falls. 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. Physical reserve was defined in 22 community-dwelling older adults with SIVCI as the unexplained residual variance in Timed-Up-and-Go test (TUG) after accounting for age, global cognitive function measured by Alzheimer’s Disease Assessment Scale-Cognitive-13 (ADAS-Cog-13), and hippocampal volume. Functional neural networks associated with PR were extrapolated as TUG-correlated network connectivity maps computed using general linear models that removed the effects of age, ADAS-Cog-13, and hippocampus volume. Subsequent analyses examined whether PR and its associated brain networks moderated the relationship between WMH and postural balance under two conditions – eyes open while standing on foam (EOF) and eyes open while standing on floor (EONF). Physical reserve and its associated functional neural networks - frontoparietal network (FPN) and default mode network (DMN) - significantly moderated the association between WMH and postural balance. Specifically, in those with high PR, postural balance was maintained regardless of WMH load while in 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".