AEROBIC TRAINING, THE DEFAULT MODE NETWORK, AND COGNITION IN OLDER ADULTS WITH MILD VASCULAR COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract Aerobic training has been shown to be effective at improving cognitive and brain outcomes in older adults with mild subcortical ischemic vascular cognitive impairment (SIVCI). However, uncertainty remains regarding the underlying neurobiological mechanisms by which exercise elicits these improvements in cognition. Increased aberrant functional connectivity of the default mode network has been highlighted as a factor contributing to cognitive decline in older adults with cognitive impairment. Greater connectivity of the DMN at rest is associated with poorer performance on attention-demanding tasks, indicative of a lack of ability to deactivate the network on task. Our previous work on a randomized controlled trial of participants with mild SIVCI, demonstrated that 6-months of thrice weekly aerobic training led to improved global cognitive function, as measured by Alzheimer’s disease Assessment Scale-Cognitive subscale (ADAS-Cog), compared with a health education program. Thus, we conducted secondary analyses to investigate whether these changes in global cognitive function were associated with changes in resting state DMN connectivity. A subsample of 21 participants underwent a resting state functional magnetic resonance imaging (fMRI) scan before and after trial completion. Change in resting state DMN connectivity was found to significantly predict change in ADAS-Cog score (β = -.442, p=.038) after controlling for age, intervention group, and baseline functional capacity (R2=.467, F(4,16)= 3.507, p=.031). These findings suggest that functional connectivity of the DMN may underlie changes in global cognitive function. Furthermore, aerobic exercise is a promising intervention by which to elicit these changes in older adults with mild SIVCI.
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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".