BRAIN FUNCTIONAL CONNECTIVITY IN OLDER ADULTS WITH MCI: DO FALLS MATTER?
Bibliographic record
Abstract
Abstract Older adults with mild cognitive impairment (MCI) are at an elevated risk of falls. We conducted a pilot longitudinal observational study to examine the natural course of brain intrinsic functional connectivity (FC) and cognitive function changes in association to falling history in older adults with MCI. 15 MCI participants (mean age 75.9, range 67-86) included 10 non-fallers and 5 fallers (minimum two falls in the previous 12 months with one in the last 6 months) from Metro Vancouver, BC, Canada. At study entry and 1-year follow-up, participants completed brain scanning session of structural MRI and resting state (RS) functional MRI, the Montreal Cognitive Assessment (MoCA), and the Mini Mental State Examination (MMSE). Results indicated an interaction between time (baseline vs. follow-up) and falls history on RS-FC in individuals with MCI (p<0.001). At 1-year follow-up, MCI non-fallers showed increased FC between frontal, parietal and occipital cortex (from baseline R=0.141 to follow-up R=0.321) and lack of decline on cognitive measures. Meanwhile, MCI fallers showed weakening of FC between those brain regions (from baseline R=0.314 to follow-up R=0.201) with simultaneous cognitive deterioration. Significant relationships between FC strength and cognitive status existed only at follow-up (R=0.525, p<0.05), suggesting that the triggered functional compensatory brain mechanisms in MCI non-fallers are not successfully executed in MCI fallers. Together, our pilot data suggest that older adults with MCI who fall show more advanced brain functional degradation with adjacent cognitive decline as compared to MCI individuals who do not fall.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".