ArticleCommunications biology2025
Automated detection of complex zebrafish seizure behavior at scale.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- In Vivo Characterization of Synthetic Cannabidiol Analogs for Seizure Suppression in Zebrafish.ACS chemical neuroscience · 2026Article
- Planarian behavioral screening is a useful invertebrate model for evaluating seizurogenic chemicals.Neurotoxicology · 2026Article
- Employing zebrafish to understand genetic drivers of epilepsy-related comorbid behaviors.Frontiers in pharmacology · 2026Article
- Zebrafish as a translational platform for investigating multi-organ pharmacological interactions of traditional Chinese medicine.Frontiers in pharmacology · 2026Review
- Early death and neuronal abnormalities inbioRxiv : the preprint server for biology · 2025Article
- Planarian behavioral screening is a useful invertebrate model for evaluating seizurogenic chemicals.bioRxiv : the preprint server for biology · 2025Article
- High throughput machine learning pipeline to characterize larval zebrafish motor behavior.PloS one · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Convulsive seizure behaviors are a hallmark feature of epilepsy, but automated detection of these events in freely moving animals is difficult. Here, we employed a high-resolution multi-camera array microscope with high-speed video acquisition and custom supervised machine learning (ML) for automated detection of larval zebrafish between 3- and 7-days post-fertilization (dpf). We assessed data from over 2700 zebrafish either exposed to a chemoconvulsant (pentylenetetrazole, PTZ) or genetic zebrafish lines representing Developmental Epileptic Encephalopathy (DEE) syndromes. Using eight-point skeletal body pose estimation for tracking individual larvae arrayed in a 96-well format, we report reliable, quantitative and age-dependent changes in maximum swim speed, as well as eye-, head- and tail- angle kinematics. Finally, we employed an ML-based algorithm to automatically identify normal and abnormal behaviors in an unbiased manner. Our results offer a robust framework for automated detection of zebrafish seizure-associated behaviors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.