Structural and genetic determinants of zebrafish functional brain networks
Bibliographic record
Abstract
Network science has revealed universal brain connectivity principles across species. However, several macroscopic network features established in human neuroimaging studies remain underexplored at cellular scales in small animal models. Here, we use whole-brain calcium imaging in larval zebrafish to investigate the structural and genetic basis of functional brain networks. Mesoscopic functional connectivity (FC) robustly captures the individuality of larvae and reflects structural connectivity (SC) derived from single-neuron reconstructions. Several connectome properties, including diffusion mechanisms and indirect pathways, predict interregional correlations. SC and FC share a hierarchical modular architecture, with structural modules shaping spontaneous and stimulus-driven activity patterns. Visual stimuli and tail monitoring reveal a functional gradient that coincides with sensorimotor functions. Last, regional expression levels of specific genes predict interregional FC. Our findings reproduce key mammalian brain network features, demonstrating larval zebrafish as a powerful model for studying large-scale network phenomena in a small and optically accessible vertebrate brain.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".