Deep Learning-based Animal Behavior Analysis: Insights from Mouse Chronic Pain Models

TL;DR

This paper introduces an automated, label-free framework using a universal action space projector to analyze mouse pain behaviors, outperforming humans and existing methods in classifying chronic pain types and assessing drug efficacy.

Contribution

The study presents a novel deep learning approach that automatically extracts behavioral features from videos without human labels, improving accuracy in pain classification and drug testing.

Findings

Achieved 48.41% accuracy in 15-class pain classification, surpassing humans and B-SOiD.

Reaches 73.1% accuracy in 3-category pain classification, outperforming human experts.

Successfully identified drug efficacy differences consistent with existing literature.

Abstract

Assessing chronic pain behavior in mice is critical for preclinical studies. However, existing methods mostly rely on manual labeling of behavioral features, and humans lack a clear understanding of which behaviors best represent chronic pain. For this reason, existing methods struggle to accurately capture the insidious and persistent behavioral changes in chronic pain. This study proposes a framework to automatically discover features related to chronic pain without relying on human-defined action labels. Our method uses universal action space projector to automatically extract mouse action features, and avoids the potential bias of human labeling by retaining the rich behavioral information in the original video. In this paper, we also collected a mouse pain behavior dataset that captures the disease progression of both neuropathic and inflammatory pain across multiple time points. Our method achieves 48.41\% accuracy in a 15-class pain classification task, significantly outperforming human experts (21.33\%) and the widely used method B-SOiD (30.52\%). Furthermore, when the classification is simplified to only three categories, i.e., neuropathic pain, inflammatory pain, and no pain, then our method achieves an accuracy of 73.1\%, which is notably higher than that of human experts (48\%) and B-SOiD (58.43\%). Finally, our method revealed differences in drug efficacy for different types of pain on zero-shot Gabapentin drug testing, and the results were consistent with past drug efficacy literature. This study demonstrates the potential clinical application of our method, which can provide new insights into pain research and related drug development.