Intra- and extra-cranial effects of transient blood pressure changes on brain near-infrared spectroscopy (NIRS) measurements
Bibliographic record
Abstract
Brain near-infrared spectroscopy (NIRS) is an emerging neurophysiological tool that combines straightforward activity localization with cost-economy, portability and patient compatibility. NIRS is proving its empirical utility across specific cognitive and emotional paradigms. However, a potential limitation is that it is not only sensitive to haemodynamic changes taking place in the cortex, and task-related cardiovascular responses expressed in the perfusion of extracranial layers may be confounding. Existing literature reports correlations between brain NIRS and systemic blood pressure, yet it falls short of establishing whether in normal participants the blood pressure changes encountered in experimental settings can have confounding effects. Here, we tested this hypothesis by performing two experimental manipulations while recording from superficial occipital cortex, encompassing striate and extrastriate regions. Visual stimulation with reversing chequerboards evoked cortical haemodynamic responses. Simultaneously and independently, transient systemic blood pressure changes were generated through rapid arm-raising. Shallow-penetration NIRS recordings, probing only extra-cerebral tissues, highlighted close haemodynamic coupling with blood pressure. A different coupling pattern was observed in deep-penetration recordings directed at haemodynamic signals from visual cortex. In absence of blood-pressure changes, NIRS signals tracked differences in visual stimulus duration. However when blood pressure was actively manipulated, this effect was absent and replaced by a very large pressure-related response. Our observations demonstrate that blood pressure fluctuations can exert confounding effects on brain NIRS, through expression in extracranial tissues and within the brain itself. We highlight the necessity for continuous blood pressure monitoring alongside brain NIRS, and for further research on methods to correct for physiological confounds.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".