Differences in cerebral oxygenation between healthy older and younger adults during the Multi‐Source Interference Task
Bibliographic record
Abstract
Abstract Background Cognitive changes that accompany normal aging include declines in processing speed and executive function. Further research is needed to characterize the neurobiological underpinnings of these cognitive changes in older adulthood. The current study used functional near infrared spectroscopy (fNIRS), a neuroimaging technique that measures oxygenated hemoglobin (HbO), to examine differences in cerebral oxygenation between healthy younger adults (YA) and older adults (OA) during a measure of cognitive interference. Method There were 34 participants sampled from two age group: YA (Mean age =28.1 years, SD=2.8, F=9) and OA (Mean age =70.9 years, SD=5.4, F=9). All participants were native English speakers, neurologically healthy, and had normal or corrected to normal vision. Participants completed the Multi‐Source Interference Task, a measure of cognitive interference with high and low‐demand conditions, while undergoing fNIRS recordings by a TechEn CW6 system with 34‐source‐detector channels over the PFC. The functional activation patterns, accuracy, and reaction time were compared between groups for each condition. Result The functional results demonstrated a significant age‐related increase in HbO for OA in both conditions (p < 0.05). In the control condition, OA demonstrated increased HbO in 10 channels covering the left and mid‐anterior PFC in comparison to YA. In the interference condition, OA demonstrated increased HbO in 6 channels covering the mid‐anterior PFC in comparison to YA. During the control condition, OA and YA had the same accuracy (YA Mean= 0.99, OA Mean= 0.99, p>0.33), but OA were significantly slower (YA Mean= 545.63, OA Mean= 768.04, p<0.0001). During the interference condition, OA had lower accuracy (YA Mean=0.97, OA Mean=0.9, p< 0.0001) and were significantly slower (YA Mean=891.31, OA Mean=1114.5, p< 0.0001) than YA. Conclusion The results from this study show that OA require greater activation in mid‐PFC regions, across both low and high‐demand conditions, relative to YA. These results are consistent with cognitive aging theories such as the compensation‐related utilization of neural circuits hypothesis (CRUNCH). Specifically, the findings suggest that OA recruit additional neural resources to achieve similar behavioural performance during low‐levels of cognitive interference, but that this same compensation may be insufficient to support behavioural performance at higher levels of interference.
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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 | 0.000 | 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".