Functional and pharmacological analyses of visual habituation learning in larval zebrafish

Tensor analysis of animal behavior by matricization and feature selection

Contemporary neurobehavior research often collects multi-dimensional tensor (MDT) data, consisting of time-series measurements for multiple features from multiple animals subjected to various perturbations. Proper analysis of the MDT data can facilitate the dissection of the underlying neural circuitry driving the behavior. However, many common approaches for MDT analysis, such as tensor decomposition, often yield results that are difficult to interpret and not directly compatible with standard multivariate analysis (MVA), which is designed for simpler, lower-dimensional data structures. To address this issue, dimensionality reduction techniques, including matricization methods such as Index Construction and Feature Concatenation, are applied to transform all or a subset of the features in the MDT into a lower-dimensional tensor, commonly a 2-dimensional tensor (2DT), that is compatible with MVA. However, the matricization methods may exclude information from the MDT features or create too many 2DT features that introduce spurious noise to the downstream analyses. Their impacts on the downstream MVA performance remain elusive. In this study, we systematically evaluated different approaches for matricization and feature selection and their impacts on MVA performance using an MDT dataset of zebrafish visual- motor response collected from wild-types (WTs) and visually-impaired mutants. We matricized the MDT dataset using various Index Construction and Feature Concatenation methods, then identified informative 2DT features using the filter and embedded methods. To evaluate these feature-selection approaches, we conducted a classification task distinguishing WT and visually-impaired zebrafish by multiple classifiers. We then assessed classification performance with cross-validation and holdout validation. We found that most classifiers performed the best when using all 2DT features matricized by Feature Concatenation and selected by the embedded method. The results also revealed unique behavioral differences between the WTs and visually-impaired mutants that were not identified by standard MVA or MDT analysis. Our results demonstrate the utility of analyzing MDT behavioral data by matricization and feature selection.

Serotonin neuromodulation directs optic nerve regeneration

Optic nerve (ON) regeneration in mammalian systems is limited by an overshadowing dominance of inhibitory factors. This has severely hampered the identification of pro-regenerative pathways. Here, we take advantage of the regenerative capacity of larval zebrafish to identify pathways that promote ON regeneration. From a small molecule screen, we identified modulators of serotonin (5-HT) signaling that inhibit ON regeneration. We find several serotonin type-1 receptor genes are expressed in RGC neurons during regeneration and that inhibiting 5-HT1 receptors or components of the 5-HT pathway selectively impedes ON regeneration. We show that 5-HT1 receptor signaling is dispensable during ON development yet is critical for regenerating axons to emerge from the injury site. Blocking 5-HT receptors once ON axons have crossed the chiasm does not inhibit regeneration, suggesting a selective role for 5-HT receptor signaling early during ON regeneration. Finally, we show that agonist-mediated activation of 5-HT1 receptors leads to enhanced and ectopic axonal regrowth. Combined, our results provide evidence for mechanisms through which serotonin-dependent neuromodulation directs ON regeneration in vivo.

pi_tailtrack: A compact, inexpensive and open-source behaviour-tracking system for head-restrained zebrafish

Quantifying animal behaviour during microscopy is crucial to associate optically recorded neural activity with behavioural outputs and states. Here, I describe an imaging and tracking system for head-restrained larval zebrafish compatible with functional microscopy. This system is based on the Raspberry Pi computer, Pi NoIR camera and open-source software for the real-time tail segmentation and skeletonization of the zebrafish tail at over 100 Hz. This allows for precise and long-term analyses of swimming behaviour, which can be related to functional signals recorded in individual neurons. This system offers a simple but performant solution for quantifying the behaviour of head-restrained larval zebrafish, which can be built for 340€.