Neurovascular coupling on trial: How the number of trials completed impacts the accuracy and precision of temporally derived neurovascular coupling estimates
Bibliographic record
Abstract
Standard practices for quantifying neurovascular coupling (NVC) with transcranial Doppler ultrasound (TCD) require participants to complete one-to-ten repetitive trials. However, limited empirical evidence exists regarding how the number of trials completed influences the validity and reliability of temporally derived NVC metrics. Secondary analyses was performed on 60 young healthy participants (30 females/30 males) who completed eight cyclical eyes-closed (20-seconds), eyes-open (40-seconds) NVC trials, using the “Where’s Waldo?” visual paradigm. TCD data was obtained in posterior and middle cerebral arteries (PCA and MCA, respectively). The within-day (n = 11) and between-day ( n = 17) reliability were assessed at seven- and three-time points, respectively. Repeat testing from the reliability aims were also used for the concurrent validity analysis ( n = 160). PCA metrics (i.e., baseline, peak, percent increase, and area-under-the-curve) demonstrated five trials produced excellent intraclass correlation coefficient (ICC) 95% confidence intervals for validity and within-day reliability (>0.900), whereas between-day reliability was good-to-excellent (>0.750). Likewise, 95% confidence intervals for coefficient of variation (CoV) measures ranged from acceptable (<20%) to excellent (<5%) with five-or-more trials. Employing fewer than five trials produced poor/unacceptable ICC and CoV metrics. Future NVC, TCD-based research should therefore have participants complete a minimum of five trials when quantifying the NVC response with TCD via a “ Where’s Waldo?” paradigm.
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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 | 0.086 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".