Whole-brain optical imaging in zebrafish larvae to investigate neural circuit development and connectivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it