Statistical mapping analysis of serotonin synthesis images generated in healthy volunteers using positron-emission tomography and alpha-[11C]methyl-L-tryptophan.
Bibliographic record
Abstract
OBJECTIVES: To assess the suitability of analyzing functional images of brain serotonin (5-HT) synthesis with statistical parametric mapping (SPM), and to investigate further possible sex-related regional differences. DESIGN: Prospective study. PARTICIPANTS: Six healthy men and 5 healthy women. INTERVENTION: Participants' brains were scanned with positron-emission tomography (PET) after intravenous injection of alpha-[11C]methyl-L-tryptophan (alpha-[11C]MTrp). OUTCOME MEASURES: Tissue radioactivity images were converted into functional images using the Patlak plot approach, and analyzed with 2 methods for global normalization in the SPM program: proportional scaling and analysis of covariance (ANCOVA). RESULTS: The data structure suggests that PET alpha-[11C]MTrp data meet the criteria for analysis with SPM, and that the proportional scaling method is more appropriate than the ANCOVA method for normalization. Regional differences in 5-HT synthesis were identified between men and women, and the significance of these findings was supported by region of interest (ROI) analyses. CONCLUSION: SPM analyses of PET alpha-[11C]MTrp data may be of value for identifying regional differences in brain 5-HT synthesis between groups, and in investigating the effects of psychotropic drugs. Since we found regional differences between male and female subjects, men and women should not be grouped for data analysis in PET alpha-[11C]MTrp studies.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".