Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype using Behaviour

TL;DR

This study develops a neural network-based method to classify larval zebrafish genotypes by behaviour, providing a new pipeline that enhances analysis of high-dimensional behavioural data and offers interpretability insights.

Contribution

It introduces a multi-layer perceptron approach combined with integrated gradients for classifying zebrafish genotypes, advancing behavioural analysis in disease models.

Findings

Accurately classifies zebrafish genotypes using behavioural features.

Provides interpretability of feature impact on classification.

Demonstrates effectiveness in distinguishing Parkinson's disease models.

Abstract

Zebrafish are a common model organism used to identify new disease therapeutics. High-throughput drug screens can be performed on larval zebrafish in multi-well plates by observing changes in behaviour following a treatment. Analysis of this behaviour can be difficult, however, due to the high dimensionality of the data obtained. Statistical analysis of individual statistics (such as the distance travelled) is generally not powerful enough to detect meaningful differences between treatment groups. Here, we propose a method for classifying zebrafish models of Parkinson's disease by genotype at 5 days old. Using a set of 2D behavioural features, we train a multi-layer perceptron neural network. We further show that the use of integrated gradients can give insight into the impact of each behaviour feature on genotype classifications by the model. In this way, we provide a novel pipeline for classifying zebrafish larvae, beginning with feature preparation and ending with an impact analysis of said features.