3-D-365 - Investigation of periaqueductal gray circuitry in larval zebrafish
Bibliographic record
Abstract
Authors: Michael Martin¹, Jordan Guerguiev¹, Nicholas Guilbeault², Venkatesh Krishna¹, Tod Thiele¹ ¹University of Toronto, ²University of Toronto Scarborough Abstract: As animals move through their environment, they work towards their goals and to avoid danger. If an animal detects a threat, it must decide on an appropriate response. The responses appear different between animals; however they all aim to increase the organism's chance of survival. The neural structures involved in these decisions and responses are so crucial that they are present in organisms across the animal kingdom. One such structure is the periaqueductal gray (PAG), found in the vertebrate midbrain. It is essential for any behavioural response to threatening stimuli, but due to its location deep in the brain, simultaneous investigation of the entire PAG has proved challenging. Larval zebrafish offer an excellent opportunity for in vivo imaging of the entire PAG due to their small size, optical accessibility, and the genetic tools available to researchers. To investigate PAG functional activity in the larval zebrafish brain I have created an experimental setup which allows for simultaneous presentation of visual stimuli, and observation of animal behaviour and neuronal activity. Additionally, I have developed an analysis pipeline for unsupervised clustering of neurons based on their activity to identify behaviour induced shifts in patterns of activity. I have identified a region in the midbrain which exhibits sustained activity in response to threatening stimuli and stains for canonical markers of the PAG, namely rln3 and penkA. Further investigation of this region will provide a more comprehensive understanding of how activity of the entire PAG affects behaviour.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 0.001 |
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".