Quantification of motor behaviour in freely moving rodents

Abstract: Systems evaluating potential treatment methods for Parkinson’s disease and chronic pain conditions, using rodent experimental models, are highly needed. Currently used systems are aiming to evaluate these kinds of treatment methods by analyzing the animal’s motor behavior. These systems either generate binary data or multifaceted data for when the rodent is spatially limited or data for when the rodent is less spatially limited, i.e. moving in an open field, that are less faceted. In this thesis a setup was built by combining setups from the currently used systems, to enable high faceted data for when the rodent is moving in an open field. Further, the data generated from this setup was interpreted by evaluating and comparing image analysis methods, using two different software. The interpretation of the data aimed to generate parameters describing the rodent’s motor behavior by detection and classification of the rodent and its various body parts and paws. The first image analysis method is based on blob detection and was evaluated using MATLAB. This method resulted in insufficient parameter values, but with potential for development regarding the evaluation of the pressure load of the paws. Further, this method showed great potential in classifying the rodent’s body parts and paws. This method therefore has the potential to be used with the built setup as a tool in motor behavior analysis, such as gait pattern and body posture analysis. The second image analysis method is deep learning based segmentation, object tracking, and classification and was evaluated using DeepLabCut. This method resulted in insufficiency in the detection and classification of the rodent’s paws, but showed potential in the detection and classification of the body parts. This method therefore has the potential to be used as a tool in motor behavior analysis when analyzing the rodent’s body posture and movement pattern, for instance, in combination with the first method.