Two Calcium Imaging Analysis Workflows for Identifying Coordinated Networks underlying C. elegans Locomotion
Bibliographic record
Abstract
Though previous research has elucidated the Central Pattern Generator (CPG) networks governing backward and forward crawling in the C. elegans body below the neck, an undiscovered CPG controlling the head likely exists. Calcium imaging constitutes an effective means for visualising neuronal activity, useful for identifying candidate CPGs and neuronal assemblies. I developed SNCI_track, an autotracker wrapper and interface for proofreading of small-number calcium imaging (SNCI) experiments. SNCI_track incurs few autotracking errors and enables fast proofreading, improving the time-until-results of an SNCI workflow. Toward identifying candidate oscillators and neuronal assemblies in the head, I implemented a collaborator-authored pan-neuronal calcium imaging (PNCI) workflow on a single unc-13(s69); unc-2(hp647); hpIs675 whole-brain recording of 135 neurons. Qualitative appraisal identifies 12 neuronal assemblies in the recording, two of which may be oscillators. This thesis demonstrates use of two calcium imaging analysis workflows and preliminary results that may elucidate new features of the C. elegans head locomotory network.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".