Development and applications of data analysis tools for multimodal positron emission tomography and magnetic resonance imaging sytems
Bibliographic record
Abstract
Two experiments (𝑁 = 5, 𝑁 = 2) were performed at the Magnetic Resonance Microscopy Centre (MRMC) in Winnipeg (MB) in an attempt to use simultaneous positron emission tomography and magnetic resonance (PET-MR) imaging to demonstrate the in vivo selective activation of serotonergic 5-HT neurons in rats. These experiments take advantage of a new imaging system developed by Cubresa Inc (Winnipeg, MB) called ‘NuPET’. This device is an MR-compatible PET insert which is placed around the subject while they are within the bore of a MR device. That allows simultaneous PET-MR imaging. To activate 5-HT neurons selectively we use designer receptors exclusively active by designer drug (DREADD) technology. These DREADDs are designer G-protein-coupled receptors. Neurons at the site of a stereotactic injection are transfected with a viral vector containing the proteins necessary to force expression of DREADDs in genetically modified rats. These may then be activated by administering the drug clozapine-N-oxide (CNO). This technique allows for precise spatiotemporal control of receptor signaling. The MRMC is highly experienced in MR experiments, but had no in-house capability to analyze the data gathered during PET-MR studies. This thesis presents the development and application of a custom-designed workflow and collection of Matlab scripts which organizes and streamlines the tasks associated with PET-MR post-processing and analysis. This is intended as a tool that may be used for any future PET-MR experiments at the MRMC, and be continually expanded as the needs of the lab grow more sophisticated. In the application of the Slapdash workflow, it was determined that we cannot confirm that the experiments were able to image the in vivo activation of serotonergic neurons. Statistical analysis performed with paired-sample t-testing reveals statistically significant differences in activity between initial and subsequent PET frames throughout both experiments, but numerous methodological issues mar our ability to draw any meaningful conclusions from the data.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".