Cerebral serotonin transporter measurements with [ <sup>11</sup> C]DASB: A review on acquisition and preprocessing across 21 PET centres
Bibliographic record
Abstract
Positron Emission Tomography (PET) imaging has become a prominent tool to capture the spatiotemporal distribution of neurotransmitters and receptors in the brain. The outcome of a PET study can, however, potentially be obscured by suboptimal and/or inconsistent choices made in complex processing pipelines required to reach a quantitative estimate of radioligand binding. Variations in subject selection, experimental design, data acquisition, preprocessing, and statistical analysis may lead to different outcomes and neurobiological interpretations. We here review the approaches used in 105 original research articles published by 21 different PET centres, using the tracer [ 11 C]DASB for quantification of cerebral serotonin transporter binding, as an exemplary case. We highlight and quantify the impact of the remarkable variety of ways in which researchers are currently conducting their studies, while implicitly expecting generalizable results across research groups. Our review provides evidence that the foundation for a given choice of a preprocessing pipeline seems to be an overlooked aspect in modern PET neuroscience. Furthermore, we believe that a thorough testing of pipeline performance is necessary to produce reproducible research outcomes, avoiding biased results and allowing for better understanding of human brain function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".