Whole-brain optical imaging in zebrafish larvae to investigate neural circuit development and connectivity
Bibliographic record
Abstract
To learn more on the factors that govern the development of neural circuits, both in structural and functional terms, we use whole brain two-photon imaging on larval zebrafish that express a pan-neuronal genetically-encoded calcium indicator GCaMP6s. Using a resonant scanner and piezo driven objective, we record neural activity (GCaMP6 fluorescence) from up to ~50% of the whole neuronal population (~100,000 neurons), while simultaneously conveying visual stimulation using a screen oriented towards the head-restrained larva in agarose. This experimental paradigm leverages the early-developing visual system of the zebrafish to evoke reproducible neuronal responses and behavioral outputs across individuals. Abrupt changes in illumination induce navigational tail movements, which are monitored using a high-speed camera to identify distinct behavioral modules and their neural correlates. By varying the temporal properties of visual stimuli, we also probe the neural mechanisms of habituation and anticipation. Using graph theory, functional networks are generated from spontaneous brain activity recordings, which are then paired with the zebrafish structural connectome (Kunst et al., Neuron, 2019) in order to gain fundamental insight on the interaction between structure and function in vertebrate brain networks. Our dual spontaneous/stimulus-evoked experimental framework will be used to compare fish across different developing conditions, namely germ-free fish, to observe the impact of gut microbiota on brain connectivity, sensorimotor integration and behavior.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".