ArticleCell reports methods2025
Visual detection of seizures in mice using supervised machine learning.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Anesthesia unconsciousness recruits dynamic parabrachial activity within a distributed brain-wide ensemble.bioRxiv : the preprint server for biology · 2026Article
- Feature library for behavioural characterization of early and late seizures in an experimental model of post-traumatic epilepsy.Journal of neuroscience methods · 2026Article
- Article
- Refining drug screening with automated home cage monitoring: a crossover study in socially housed rats.Frontiers in toxicology · 2026Article
- 25 years of applying the 3Rs principles in safety pharmacology: success stories and future perspectives.Frontiers in physiology · 2026Review
- Behavior Decoding Delineates Seizure Microfeatures and Associated Sudden Death Risks in Mouse Models of Epilepsy.Annals of neurology · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Seizures are caused by abnormal synchronous brain activity. The resulting changes in muscle tone, such as twitching, stiffness, or jerking, are used in visual scoring systems such as the Racine scale to quantify seizure intensity. However, visual inspection is time consuming, low throughput, and partially subjective, and there is a need for scalable and rigorous quantitative approaches. We used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from non-invasive video data. Using the pentylenetetrazole (PTZ)-induced seizure model in mice, we trained video-only classifiers to predict ictal events and combined these events to predict composite seizure intensity for a recording session, as well as time-localized seizure intensity scores. Our results show that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, non-invasive, and standardized seizure scoring for neurogenetics and therapeutic discovery.
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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 Socratic graph.