Synergistic Benefits of Combined Aerobic and Cognitive Training on Fluid Intelligence and the Role of IGF-1 in Chronic Stroke
Bibliographic record
Abstract
Background. Paired exercise and cognitive training have the potential to enhance cognition by “priming” the brain and upregulating neurotrophins. Methods. Two-site randomized controlled trial. Fifty-two patients >6 months poststroke with concerns about cognitive impairment trained 50 to 70 minutes, 3× week for 10 weeks with 12-week follow-up. Participants were randomized to 1 of 2 physical interventions: Aerobic (>60% VO 2peak using <10% body weight–supported treadmill) or Activity (range of movement and functional tasks). Exercise was paired with 1 of 2 cognitive interventions (computerized dual working memory training [COG] or control computer games [Games]). The primary outcome for the 4 groups (Aerobic + COG, Aerobic + Games, Activity + COG, and Activity + Games) was fluid intelligence measured using Raven’s Progressive Matrices Test administered at baseline, posttraining, and 3-month follow-up. Serum neurotrophins collected at one site (N = 30) included brain-derived neurotrophic factor (BDNF) at rest (BDNF resting ) and after a graded exercise test (BDNF response ) and insulin-like growth factor–1 at the same timepoints (IGF-1 rest , IGF-1 response ). Results. At follow-up, fluid intelligence scores significantly improved compared to baseline in the Aerobic + COG and Activity + COG groups; however, only the Aerobic + COG group was significantly different (+47.8%) from control (Activity + Games −8.5%). Greater IGF-1 response at baseline predicted 40% of the variance in cognitive improvement. There was no effect of the interventions on BDNF resting or BDNF response ; nor was BDNF predictive of the outcome. Conclusions. Aerobic exercise combined with cognitive training improved fluid intelligence by almost 50% in patients >6 months poststroke. Participants with more robust improvements in cognition were able to upregulate higher levels of serum IGF-1 suggesting that this neurotrophin may be involved in behaviorally induced plasticity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".