Automated decoding the mouse body postures in a whisker nuisance test

Abnormal response to sensory stimuli characterizes multiple neuropsychiatric conditions. However, not many tools are currently available to assess somatosensory abnormalities in rodent models of brain disorders, limiting the possibilities to study cellular and molecular correlates of this phenotypic trait. To this goal, previous studies relied on the whisker nuisance test (WNt), in which freely moving mice are constantly stimulated on their whiskers using a wood stick for a set time. The whisker-guided response is then deconstructed in behavioral categories indicative of either anxiety or curiosity. Thus far, WNt was shown to be a valuable tool to investigate sensory-driven abnormalities in mouse models of autism spectrum disorders (ASD), demonstrating a solid translational validity. Nevertheless, assessment of behavioral response in the WNt is currently limited by the lack of an objective quantification method. To overcome this limitation, we developed WNt3R (Whisker Nuisance Test in 3D for Rodents), a MATLAB toolbox that uses the output of the open-source software DeepLabCut to determine discrete body postures associated with multiple ethologically-relevant behaviors. Our results show that behavioral modules identified using WNt3R reliably decode mouse body postures, outperforming the manual user, thus offering a novel and unbiased approach to study altered somatosensory function in rodents.