Hidden behavioral fingerprints in epilepsy
Open accessAbstract
Epilepsy is a major disorder affecting millions of people. Although modern electrophysiological and imaging approaches provide high-resolution access to the multi-scale brain circuit malfunctions in epilepsy, our understanding of how behavior changes with epilepsy has remained rudimentary. As a result, screening for new therapies for children and adults with devastating epilepsies still relies on the inherently subjective, semi-quantitative assessment of a handful of pre-selected behavioral signs of epilepsy in animal models. Here, we use machine learning-assisted 3D video analysis to reveal hidden behavioral phenotypes in mice with acquired and genetic epilepsies and track their alterations during post-insult epileptogenesis and in response to anti-epileptic drugs. These results show the persistent reconfiguration of behavioral fingerprints in epilepsy and indicate that they can be employed for rapid, automated anti-epileptic drug testing at scale.
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MoSeq has uncovered previously undetected patterns of spontaneous behavior associated with epileptic activity, providing a behavioral biomarker of brain dysfunction.
“For example, MoSeq has uncovered previously undetected patterns of spontaneous behavior associated with epileptic activity, providing a behavioral biomarker of brain dysfunction.9”
From Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models (2024) · cites this paper as [9]
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Miller, S. R., Luxem, K., Lauderdale, K., Nambiar, P., Honma, P. S., Ly, K. K., Bangera, S., Bullock, M., Shin, J., Kaliss, N., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114870 · Cited by 24 · see citing works
“9 MoSeq and VAME are based on unsupervised dimensionality reduction methods, which analyze the implicit structure and patterns of postural motion to identify the most relevant features that capture the spatiotemporal variability of the data to define brief stereotypical postural units.”
- Authors
- Gschwind, T., Zeine, A., Raikov, I., Markowitz, J. E., Gillis, W. F., Felong, S., Isom, L. L., Datta, S. R., & Soltesz, I.
- Affiliations
- Stanford Medicine (US)
- Harvard University (US)
- University of Michigan (US)
- Corresponding author
- Gschwind, Tilo
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2023-05
- Volume
- 111
- Issue
- 9
- Pages
- 1440-1452.e5
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC10164063
- OpenAlex
- W4321782624
- Cited by
- 47
- References
- 3
- Citations by year
- 2026: 14 · 2025: 20 · 2024: 8 · 2023: 5
- FWCI
- 9.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Neuron (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (NINDSR01NS114020, NINDSR37NS076752)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (174811, 186757)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmacological Effects and Toxicity Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- epilepsy, behavioral phenotypes, 3D video analysis, machine learning, epileptogenesis, acquired epilepsy, genetic epilepsy, mouse model
- MeSH terms
- Animals, Brain, Disease Models, Animal, Epilepsy, Mice
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