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Collective Behavior Emerges from Genetically Controlled Simple Behavioral Motifs in Zebrafish

Harpaz et al., Science Advances. 2021.

https://www.science.org/doi/10.1126/sciadv.abi7460

Harpaz et al. report that zebrafish regulate their proximity and alignment with each other at early larval stages. Two visual responses (one measuring relative visual field occupancy and one accounting for global visual motion), account for emerging group behavior. Mutations in genes known to affect social behavior in humans perturb these reflexes in individual larval zebrafish and change their emergent collective behaviors. Model simulations show that changes in these two responses in individual mutant animals predict well the distinctive collective patterns that emerge in a group. Hence, group behaviors reflect in part genetically defined primitive sensorimotor “motifs” evident in young larvae. Supported by ORIP (R43OD024879, R44OD024879) and NINDS.

Deep Learning-Based Framework for Cardiac Function Assessment in Embryonic Zebrafish from Heart Beating Videos

Naderi et al., Computers in Biology and Medicine. 2021.

https://www.sciencedirect.com/science/article/pii/S0010482521003590

Zebrafish is a powerful model system for a host of biological investigations, cardiovascular studies, and genetic screening. However, the current methods for quantifying and monitoring zebrafish cardiac functions involve tedious manual work and inconsistent estimations. Naderi et al. developed a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fractional shortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant zebrafish embryos. The framework could be widely applicable with any laboratory resources, and the automatic feature holds promise to enable efficient, consistent, and reliable processing and analysis capacity. Supported by ORIP (R44OD024874)