Pan-neuronal imaging in roaming <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
We present an imaging system for pan-neuronal recording in crawling Caenorhabditis elegans. A spinning disk confocal microscope, modified for automated tracking of the C. elegans head ganglia, simultaneously records the activity and position of ∼80 neurons that coexpress cytoplasmic calcium indicator GCaMP6s and nuclear localized red fluorescent protein at 10 volumes per second. We developed a behavioral analysis algorithm that maps the movements of the head ganglia to the animal's posture and locomotion. Image registration and analysis software automatically assigns an index to each nucleus and calculates the corresponding calcium signal. Neurons with highly stereotyped positions can be associated with unique indexes and subsequently identified using an atlas of the worm nervous system. To test our system, we analyzed the brainwide activity patterns of moving worms subjected to thermosensory inputs. We demonstrate that our setup is able to uncover representations of sensory input and motor output of individual neurons from brainwide dynamics. Our imaging setup and analysis pipeline should facilitate mapping circuits for sensory to motor transformation in transparent behaving animals such as C. elegans and Drosophila larva.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".