P249 Brain-wide calcium imaging in zebrafish and generative network modelling reveal cell-level functional network properties of seizure susceptibility
Wei Qin*1, Jessica Beevis2, Maya Wilde2, Sarah Stednitz1, Josh Arnold2, Itia Favre-Bulle2, Ellen Hoffman3, Ethan K. Scott1
1 Department of Anatomy and Physiology, University of Melbourne, VIC, Australia
2 Queensland Brain Institute, University of Queensland, QLD, Australia
3 Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, USA
*Email: wei.qin@unimelb.edu.au
Introduction
Epilepsy causes recurrent seizures, but the exact mechanisms are still unclear. Traditional methods using data from primates or rodents struggle to resolve individual cell activity while tracking whole-network dynamics. Capturing the interactions of individual neurons within brain-wide networks could greatly enhance our understanding. Zebrafish, which share genetic and physiological similarities with humans, can exhibit seizure-like behaviors when exposed to drugs like PTZ, which blocks inhibitory GABAergic signaling and induces hyperexcitability [2]. Zebrafish and calcium imaging enable simultaneous in-vivo recording of neuronal activity across the brain at cellular resolution, offering a valuable approach to studying epilepsy [1].
Methods
In-vivo light-sheet calcium imaging was used to capture brain-wide cellular-resolution Calcium fluorescent data from wildtype andscn1lab(a gene implicated in Dravet Syndrome) mutant zebrafish larvae [3]. We conducted this under both baseline and PTZ conditions. Through network analyses, we statistically quantified differences in network topology and dynamics between the two genotypes. We focused on the network of active neuronal cells involved in ictogenesis at microscopic and macroscopic scales. Additionally, we developed a Generative Network Model [4] (GNM, Fig. A, Eq. 1) to explain the wiring principles governing both genotypes and the impact of thescn1labmutation on the brain-wide functional network.
Results
Our study reveals significant changes in brain network connectivity, showing thatscn1labmutations impact brain structure and function. The GNM at the cellular level explains the wiring principles governing the development of both genotypes (Fig. B) and the effects of PTZ on the brain-wide network. The model predicts genotypes and seizure severities for each fish before any seizure activities. This novel model also highlights brain regions associated with genotype differences (Fig. C, D), seizure severity, and overall network excitability. Combining experimental data and mathematical modeling, our approach offers a novel perspective on epileptogenesis mechanisms at a depth and resolution that traditional studies cannot achieve.
Discussion
Our study shows thatscn1lab-/-zebrafish larvae have significant brain morphology changes and increased PTZ-induced seizure susceptibility. Their network architecture mirrors PTZ-treated networks' wiring principles. Brain-wide, cellular-resolution activity data revealed notable alterations in baseline functional wiring, and PTZ administration affected network properties differently inscn1lab-/-and WT larvae, highlighting divergent neural responses. The GNM elucidated specific brain regions where the habenula, pallium, and cerebellum in Dravet Syndrome shows how multiple brain regions are affected, with the habenula influencing seizure initiation and the cerebellum regulating excitatory-inhibitory balance.
Figure 1. A. Generative network modelling (GNM) simulates wiring principles, evaluated by KS similarity. B. The model accurately classifies and predicts genotypes without relying on phenotypes. C. It assesses the contribution of each region to correct classification at each PTZ stage. D. The pallium and habenula are identified as the main contributors to the classification.
Acknowledgements
The authors would like to thank the UQBR aquatics team for maintenance of fish stocks. This project is supported by NHMRC, ARC, Simons Foundation and NIH (US).
References1. https://doi.org/10.1007/978-94-007-2888-2_40 2. https://doi.org/10.1371/journal.pone.0054166 3. https://doi.org/10.1093/braincomms/fcae135 4. Hills, T. T. (2024). Generative Network Models and Network Evolution. In: Behavioral Network Science: Language, Mind, and Society (pp. 46-60). Cambridge University Press.
