Visual detection of seizures in mice using supervised machine learning.

Seizures are caused by abnormally synchronous brain activity that can result in changes in muscle tone, such as twitching, stiffness, limpness, or rhythmic jerking. These behavioral manifestations are clear on visual inspection and the most widely used seizure scoring systems in preclinical models, such as the Racine scale in rodents, use these behavioral patterns in semiquantitative seizure intensity scores. However, visual inspection is time-consuming, low-throughput, and partially subjective, and there is a need for rigorously quantitative approaches that are scalable. In this study, we used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from noninvasive video data. Using the PTZ-induced seizure model in mice, we trained video-only classifiers to predict ictal events, combined these events to predict an univariate seizure intensity for a recording session, as well as time-varying seizure intensity scores. Our results show, for the first time, 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, noninvasive, and standardized seizure scoring for downstream applications such as neurogenetics and therapeutic discovery.

A machine-vision-based frailty index for mice.

Heterogeneity in biological aging manifests itself in health status and mortality. Frailty indices (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional studies, high-throughput approaches are necessary. Here we introduce a machine-learning-based visual FI for mice that operates on video data from an open-field assay. We use machine vision to extract morphometric, gait and other behavioral features that correlate with FI score and age. We use these features to train a regression model that accurately predicts the normalized FI score within 0.04 ± 0.002 (mean absolute error). Unnormalized, this error is 1.08 ± 0.05, which is comparable to one FI item being mis-scored by 1 point or two FI items mis-scored by 0.5 points. This visual FI provides increased reproducibility and scalability that will enable large-scale mechanistic and interventional studies of aging in mice.