Resting-state Functional Connectivity of the Motor and Cognitive Areas is Preserved in Masters Athletes
Bibliographic record
Abstract
Aging is characterized by a decline in physical and cognitive functions, often resulting in decreased quality of life. Physical activity has been suggested to potentially slow down various aspects of the aging process, a theory that has been supported by studies of Masters Athletes (MA). For example, MA usually have better cognitive and physical functions than age-matched sedentary and healthy older adults (OA), making them a valuable model to gain insights into mechanisms that promote physical and cognitive function with aging. The purpose of this study was to identify differences in resting-state functional connectivity (rs-FC) of motor and cognitive regions between MA and OA and determine if these differences in the resting brain are associated with differences in cognitive and physical performance between groups. Fifteen MA (9 males) and 12 age-matched OA (six males) were included. rs-FC images were compared to identify significant between-groups differences in brain connectivity. There was higher connectivity between the cognitive and motor networks for the OA group, whereas the MA group had stronger connectivity between different regions within the same network, both for the cognitive and the motor networks. These results are in line with the literature suggesting that aging reduces the segregation between functional networks and causes regions within the same network to be less strongly connected. High-level physical activity practiced by the MA most likely contributes to attenuating aging-related changes in brain functional connectivity, preserving clearer boundaries between different functional networks, which may ultimately favor maintenance of efficient cognitive and sensorimotor processing.
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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) | 0.002 | 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".