Brain connectivity moderating the association cognitive intraindividual variability and mobility in the cognitively frail older adults
Bibliographic record
Abstract
BACKGROUND: Cognitive frailty is the concurrent presence of mild cognitive impairment (MCI) and physical frailty, causing one to be at greater risk for cognitive decline. Cognitive intraindividual variability (IIV) is a critical component of cognition involved in the maintenance of higher-order processes under load. Greater IIV is hallmark of cognitive frailty and a marker for early signs of impaired cognition and mobility in older adults. However, the underlying neural mechanism of cognitive frailty-related decline in IIV and mobility remains unexplored. This study aimed to clarify the association between brain function, IIV, and mobility in cognitively frail older individuals. METHOD: This cross-sectional study included 17 cognitively frail older adults (CF) and 20 non-cognitively frail older adults (non-CF). Cognitive frailty was operationalized the presence of MCI (i.e., Montreal Cognitive Assessment score ≥ 18/30 and < 26/30) and physical frailty (i.e., Short Physical Performance Battery ≤ 9/12). All participants underwent clinical assessments including the Stroop Test, Trail Making Test, Timed-Up and Go test (TUG), and resting-state functional magnetic resonance imaging. Dispersion across executive tests was computed to ascertain IIV-dispersion. Analysis of covariance was used to determine group differences in IIV-dispersion, adjusting for the Functional Comorbidity Index. Moderation models were constructed to investigate the role of functional neural networks on the association between IIV-dispersion and TUG performance. RESULTS: Compared to non-CF, CF exhibited greater IIV-dispersion (p = 0.038), worse TUG performance (p <0.010), lower inter-network connectivity in the DMN, FEN, and SMN (all p <0.050), as well as reduced intra-network connectivity in the DMN and SMN (all p <0.050). Among CF, regional inter-network connectivity between the DMN and FEN (i.e., bilateral middle temporal gyrus (BMTG) and bilateral inferior frontal gyrus (BIFG)) moderated the relationship between IIV-dispersion and TUG performance (R-sq=0.427, p = 0.001, Figure 1). Specifically, compared with individuals with lower BMTG-BIFG connectivity (β=4.082, p <0.001), those with greater BMTG-BIFG connectivity (β=1.561, p = 0.002) showed greater TUG performance under higher IIV-dispersion load. These associations were not observed in non-CF. CONCLUSION: Differences in intra- and inter-network connectivity patterns in large-scale functional neural networks between CF and non-CF may underpin how IIV-dispersion negatively impacts TUG performance in cognitively frail older individuals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".