부산대학교 도서관

A hallmark of neurodegenerative diseases, such as the rare monogenic lysosomal storage disease, Niemann-Pick disease type C (NPC), is motor dysfunction. There- fore, gait analysis is frequently used in pre-clinical and clinical studies. The Catwalk system is an automated instrument that collects gait data from rodents. The system captures data on 495 variables. However, there is currently no standard process to identify disease relevant variables or indeed analyse these variables. Therefore, researchers report only a subset of Catwalk data based on assumptions, hypotheses, or the level of statistical significance. Thus, highly relevant variables might be overlooked, and this might lead to misleading or ill-informed conclusions to be drawn. In addition, any disease model that results in reduced body weight (e.g., Npc1 -/- mice) results in weak paw print signals in the Catwalk system, which will affect the quality and quantity of the data. In this thesis, I have therefore innovated a new standard process, the Evidence Based Gait Analysis Method, that not only optimises the quality and the quantity of the data but also provides a systematic unbiased basis for selecting relevant variables. Cerebellar ataxia is a key feature in Npc1 -/- mice and results from the loss of Purkinje cells in the cerebellum. The Catwalk system can detect this trait as it generates paw-oriented variables. However, these variables are insufficient to describe the whole picture of the ataxia these mice display, particularly the angle of deviation from movement in a straight trajectory. Thus, an additional walking path method was developed to complement the gait analysis method. The walking path was tracked using DeepLabCut (DLC), a deep learning algorithm, using videos recorded by the Catwalk system. The paths were analysed in four aspects, namely velocity, X-direction, Y-direction, and angular rotation. This can capture the walking phenotype of mice and the interpretation of the result is therefore straightforward. Together, these two new methods offer for the first time a detailed evaluation of gait performance underpinned by robust statistical evidence and so is a potential game changer in this field. I have applied this new and innovative approach to analysing changes in gait in two mouse models of NPC disease either untreated or treated with single or combination therapies and the data generated allow us for the first time to study in detail the differential responses of the disease to different treatments. We anticipate that this will form the basis for the rational selection of optimal combination therapies for clinical application in the future.