Denoising and DA release: effect of denoising on the ability to identify voxel-level neurophysiological response
Bibliographic record
Abstract
Parametric kinetic models such as lp-ntPET are used to estimate spatiotemporal patterns of endogenous neurotransmitter release, such as smoking-induced dopamine (DA) release. Applicability of such models is often limited by noise in the voxel-level time activity curves (TAC). Previous work has demonstrated the usefulness of HYPR-based spatial post-processing to increase neurotransmitter release detection sensitivity. Here we extend on this work by developing more realistic noise modelling and including additional denoising approaches. Using simulations and data acquired in non-human primates (NHP), we demonstrate that HYPR-based denoised reconstructions coupled with HYPR post-processing enables a ~ 7 fold reduction in the detectable size of the region with neurotransmitter release, less biased parameters of interest, and favours bolus vs. bolus + constant infusion protocols.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".