Genotype-dependent behavioral signatures of opioid withdrawal revealed by automated behavioral quantification

Opioid withdrawal drives continued opioid use in opioid use disorder (OUD), which affected an estimated 4 million Americans in 2024. However, only one non-opioid medication is FDA-approved for withdrawal treatment, and there is great need for more diverse treatment options. Mice are powerful models to study opioid withdrawal because they allow for controlled drug and environmental exposure, express OUD-relevant phenotypes, and provide access to withdrawal-relevant tissue at exact time points. However, mouse models are hindered by their over reliance on a single genetic background (C57BL/6J). Using a single background risks limiting the translatability of preclinical studies as they are projected into genetically diverse human populations. Inferring withdrawal intensity in mice typically relies on single behaviors or indexes of behaviors, and applying these approaches across mouse genetic diversity faces a critical challenge: differing withdrawal responses could either encode differences in withdrawal severity, or differences in withdrawal-evoked behaviors independent of severity. To overcome this limitation, a more comprehensive assessment of opioid withdrawal behaviors across mouse genetic diversity is needed. Here we leverage computer vision and machine learning to automate the simultaneous quantification of many mouse behaviors during opioid withdrawal in eight diverse strains of mice. We reveal that all eight strains exhibit withdrawal-evoked behaviors, and these behaviors are sufficient to identify the withdrawal state of the mouse. However, the effect of withdrawal on individual behaviors or indexes of behaviors is not uniform across genetic diversity, and extending methods tuned in one strain risks misclassifying withdrawal in others. Finally our approach was able to distinguish graded withdrawal intensity, as defined by opioid exposurewithin3of4strainstested. We therefore conclude that opioid withdrawal behavior is profoundly shaped by genetic background, but withdrawal severity can nonetheless be established within a background. Combining automated computer-vision phenotyping with genotype-aware modeling provides a scalable path toward genetically informed withdrawal assessment in diverse mouse populations, with the ultimate goal of identifying the genetic and biological networks that modulate opioid withdrawal and may represent new targets for treating opioid use disorder.