Optimal measurements of hemodynamic response latency in fNIRS using the jackknife approach
Bibliographic record
Abstract
Functional near-infrared spectroscopy (fNIRS) permits measurements of changes in the concentration of oxygenated and deoxygenated hemoglobin, typically with a higher sampling rate than with other imaging methods based on the hemodynamic response. We examined the potential of the fNIRS technique to estimate variations in the latency of hemodynamic responses to experimental events and sought optimal methods to maximize the reliability and reproducibility of latency effects. We used Monte Carlo simulations using subsamples of real fNIRS measures to estimate the statistical power of different approaches (such as fixed threshold, percent of peak, fractional-area latency, for both individual-subject estimates and estimates from jackknife averages) to detect a known simulated latency shift. The simulations used measures of hemodynamic responses in the temporal lobe from two groups of young adult participants who listened to auditory stimuli, one with a blocked presentation design and one with an event-related design. We estimated the relative sensitivity of different latency measures and approaches to the measurement of latency effects of different magnitudes using realistic noise and signal-to-noise characteristics. In general, the jackknife approach provided the greatest statistical power to detect a known latency shift, without inflation of Type I error.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".