Cerebral pulsatility might be a driver of neurofunctional reorganization in the aging brain: an MRI and NIRS study
Bibliographic record
Abstract
Age-related increases in cerebral pulsatility are thought to stress cerebral microcirculation, with effects that may vary across different brain regions. The aging brain also undergoes neurofunctional changes to preserve and, in some cases, enhance cognitive abilities. This study investigated the association between cerebral pulsatility and neurofunctional changes in aging. Sixty healthy adults were divided into two groups of younger (aged 19-31 years) and older adults (aged 62-75 years). Participants underwent near-infrared spectroscopy (NIRS) imaging, including a resting-state recording to capture the baseline cortical cerebral pulsatility index, followed by a Stroop task to assess cortical hemodynamic-evoked activity. Phase-contrast magnetic resonance imaging (PC-MRI) was also performed to measure pulsatility in the large arteries supplying the brain. Results indicated that older adults exhibited a significant interhemispheric difference in cerebral pulsatility index, with a higher index in the left hemisphere compared to the right. In older adults, a greater interhemispheric difference in cerebral pulsatility index was associated with larger task-evoked oxyhemoglobin concentration changes in the right hemisphere and smaller changes in the left hemisphere for the Stroop task. Younger adults, in contrast, showed no significant interhemispheric difference in the cerebral pulsatility index nor a significant correlation with task-evoked activations. These findings suggest that age-related changes in cerebral pulsatility might contribute to or potentially drive functional reorganization in the aging brain. Further investigation is needed to provide more insight into this finding.
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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".