P2‐297: Aerobic exercise increases cortical white matter volume in older adults with vascular cognitive impairment: A 6‐month randomized controlled trial
Bibliographic record
Abstract
Worldwide, Sub-cortical vascular ischaemia (SVCI) is the second most common etiology contributing to cognitive impairment among older adults. Yet, SVCI may be the most treatable form of cognitive impairment as many of its risk factors can be reduced with exercise. Nevertheless, few randomized controlled trials to date have specifically assessed the efficacy of exercise training on cognitive and brain outcomes in this high-risk group. Thus, we conducted a 6-month proof-of-concept randomized controlled trial of thrice-weekly aerobic exercise training (AE) among adults with mild SVCI. A sub-set of participants underwent MRI scanning; the focus of this analysis was to investigate the effect of AE on both white matter and grey matter in this sub-set. Seventy-one adults (56-96 years) with SVCI were recruited and randomized (1:1) to one of two experimental groups: 1) 3x/week AE or 2) usual care (UC). SVCI was confirmed by: 1) evidence of subcortical white matter lesions from neuroimaging (i.e., CT or MRI); 2) a score of less than 26 on the Montreal Cognitive Assessment (MoCA); and 3) clinical assessment by neurologist. Thirty participants (16 from AE and 14 from UC) completed 3T MRI scanning both at baseline and trial completion. Scans were analyzed using FSL Freesurfer. Compared with the control group, cortical white matter volume significantly increased in the AE group (p = .039). However, total grey matter volume significantly decreased in the AE group compared with the UC group (p= .043). A 6-month AE program significantly increased white matter volume in older adults diagnosed with VCI compared with the control group. However, future studies are need to confirm our current results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".