Cardiorespiratory fitness and prefrontal cortex oxygenation during Stroop task in older males
Bibliographic record
Abstract
AIM: The aim of the current study was to assess whether executive function and prefrontal oxygenation are dependent on fitness level and age in older adults. METHODS: Twenty-four healthy males aged between 55 and 69 years old were recruited for this study. They were stratified by age, leading to the creation of two groups: 55-60 years old and 61-69 years old. A median split based on CRF created higher- and lower-fit categories of participants. Cerebral oxygenation was assessed using functional near-infrared spectroscopy (fNIRS) during a computerized Stroop task. Accuracy (% of correct responses) and reaction times (ms) were used as behavioural indicators of cognitive performances. Changes in oxygenated (∆[HbO2]) and deoxygenated (∆[HHb]) hemoglobin were measured to capture neural changes. Repeated measures ANOVAs (CRF × Age × Stroop conditions) were performed to test the null hypothesis of an absence of interaction between CRF, Age and executive performance. RESULTS: We also found an interaction between CRF and age on reaction times (p = .001), in which higher fitness levels were related to faster reaction times in the 61-69 year olds but not in the 55-60 year olds. Regarding ΔHHb, the ANOVA revealed a main effect of CRF in the right PFC (p = .04), in which higher-fit participants had a greater Δ[HHb] than the lower-fit (d = 1.5). We also found fitness by age interaction for Δ[HHb] in the right PFC (p = .04). CONCLUSION: Our results support the positive association of CRF on cerebral oxygenation and Stroop performance in healthy older males. They indicated that high-fit individuals performed better in the 61-69 year olds group, but not in the 55-60 years old group. We also observed a greater PFC oxygenation change (as measured by Δ[HHb]) in the high-fit individuals.
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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 | 0.000 |
| 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) | 0.002 | 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".