Methods for Parkinson’s rat model PET image analysis with regions of interest
Bibliographic record
Abstract
Accurate methods are required for analysis of microPET dopamine (DA) receptor or transporter images of unilaterally 6-hydroxydopamine-lesioned rat models of Parkinson's disease. Heavily lesioned striata and the cerebellum do not appear distinctly in PET images when presynaptic tracers such as [11C]-(+)-dihydrotetrabenazine (DTBZ) are used, and are difficult targets on which to place reliably regions of interest (ROIs) without additional guidance. Registration of a brain atlas to DA receptor/transporter images significantly improves reproducibility and reliability of ROI-based analyses, as measured by discrepancy between calculated binding potentials (BP) of repeated scans of the same animal, and correlation with autoradiographic binding measurements with the same tracer (DTBZ). Averaging over 3 or 5 axial planes to generate time activity curves gives equivalent reproducibility and reliability. Scan-to-scan coregistration with automated image registration (AIR) can be successful with appropriate masking. Coregistered image analysis produces statistically equivalent results to separately placing ROIs on images that are being directly compared, though coregistered images require only one set of ROIs to be placed, reducing analysis effort.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.245 | 0.108 |
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".