# Automated identification of ataxia and convulsions in hyperexcitable Shudderer mutants of the Drosophila sodium channel gene paralytic

## TL;DR

Researchers developed a machine-learning system to automatically detect abnormal behaviors in fruit flies with a mutated sodium channel gene, which causes neurological symptoms.

## Contribution

A machine-learning classifier was created to automate the detection of convulsions and ataxia in Shudderer mutants of Drosophila.

## Key findings

- The classifier reliably identified walking activity and immobilization in Shudderer mutants.
- Uncoordinated movement events were detected with moderate sensitivity and high specificity.
- Behavioral differences between Shudderer mutants and wild-type flies were characterized across temperature ranges.

## Abstract

Drosophila
carrying the
Shudderer (Shu)
allele of the voltage-gated Na
+
gene
paralytic
display spontaneous convulsions and immobilization phenotypes that are exacerbated by high temperature. To automate identification of these aberrant behaviors in
Shu
mutants, we trained a machine-learning classifier on a manually annotated dataset. The system reliably classified walking activity and immobilization periods, while uncoordinated movement events were detected with moderate sensitivity and high specificity. We then characterized the behavioral repertoire of
Shu
mutants and wild-type flies over a temperature-ramp protocol (20 – 40 °C) using the classifier. Our developments facilitate quantitative studies of environmental or genetic factors that alter behaviors characteristic of
Drosophila
models of neurological disease.