Inter-individual differences in cerebrovascular reactivity are synchronized within functional networks and tissue layers: evidence from healthy older adults and patients with hypertension
Bibliographic record
Abstract
Abstract Functional-connectivity mapping has primarily relied resting-state functional MRI (rs-fMRI), and resting-state functional networks (RSNs) have been used widely to represent interactions within brain circuits. However, recent work demonstrated that resting-state functional networks (RSNs) may co-exist with vascular networks. In this work, we clarify the nature of these vascular networks by assessing the spatial covariation structure in breath-holding-based CVR amplitude and lag in a group of healthy older adults. We demonstrate a spatial synchrony in CVR amplitude and lag co-variations across participants confined to RSNs. Such a network structure is not seen when looking at the time-variate BOLD signal response to the breathhold. a network structure is also maintained in older adults with clinical hypertension, demonstrating its robustness against vascular pathologies. CVR amplitude is also found to vary with tissue layer in the grey matter and white matter, being most variable in deep WM and least variable in superficial cortex. Conversely, CVR lag appears to be organized by fibre tracts. This work demonstrates the use of cross-participant covariation patterns in CVR as an informative way of mapping the vascular routes in the GM and WM, and also raises questions about the nature and interpretation of RSNs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".