Systematic data analysis and data mining in CatWalk gait analysis by heat mapping exemplified in rodent models for neurodegenerative diseases.

BACKGROUND: Motor impairment appears as a characteristic symptom of several diseases and injuries. Therefore, tests for analyzing motor dysfunction are widely applied across preclinical models and disease stages. Among those, gait analysis tests are commonly used, but they generate a huge number of gait parameters. Thus, complications in data analysis and reporting raise, which often leads to premature parameter selection. NEW METHODS: In order to avoid arbitrary parameter selection, we present here a systematic initial data analysis by utilizing heat-maps for data reporting. We exemplified this approach within an intervention study, as well as applied it to two longitudinal studies in rodent models related to Parkinson's disease (PD) and Huntington disease (HD). RESULTS: The systematic initial data analysis (IDA) is feasible for exploring gait parameters, both in experimental and longitudinal studies. The resulting heat maps provided a visualization of gait parameters within a single chart, highlighting important clusters of differences. COMPARISON WITH EXISTING METHOD: Often, premature parameter selection is practiced, lacking comprehensiveness. Researchers often use multiple separated graphs on distinct gait parameters for reporting. Additionally, negative results are often not reported. CONCLUSIONS: Heat mapping utilized in initial data analysis is advantageous for reporting clustered gait parameter differences in one single chart and improves data mining.

Dynamic footprint based locomotion sway assessment in α-synucleinopathic mice using Fast Fourier Transform and Low Pass Filter.

BACKGROUND: Sway is a crucial gait characteristic tightly correlated with the risk of falling in patients with Parkinsons disease (PD). So far, the swaying pattern during locomotion has not been investigated in rodent models using the analysis of dynamic footprint recording obtained from the CatWalk gait recording and analysis system. NEW METHODS: We present three methods for describing locomotion sway and apply them to footprint recordings taken from C57BL6/N wild-type mice and two different α-synuclein transgenic PD-relevant mouse models (α-synm-ko, α-synm-koxα-synh-tg). Individual locomotion data were subjected to three different signal processing analytical approaches: the first two methods are based on Fast Fourier Transform (FFT), while the third method uses Low Pass Filters (LPF). These methods use the information associated with the locomotion sway and generate sway-related parameters. RESULTS: The three proposed methods were successfully applied to the footprint recordings taken from all paws as well as from front/hind-paws separately. Nine resulting sway-related parameters were generated and successfully applied to differentiate between the mouse models under study. Namely, α-synucleinopathic mice revealed higher sway and sway itself was significantly higher in the α-synm-koxα-synh-tg mice compared to their wild-type littermates in eight of the nine sway-related parameters. COMPARISON WITH EXISTING METHOD: Previous locomotion sway index computation is based on the estimated center of mass position of mice. CONCLUSIONS: The methods presented in this study provide a sway-related gait characterization. Their application is straightforward and may lead to the identification of gait pattern derived biomarkers in rodent models of PD.

Gait analysis in a mouse model resembling Leigh disease.

Leigh disease (LD) is one of the clinical phenotypes of mitochondrial OXPHOS disorders and also known as sub-acute necrotizing encephalomyelopathy. The disease has an incidence of 1 in 77,000 live births. Symptoms typically begin early in life and prognosis for LD patients is poor. Currently, no clinically effective treatments are available. Suitable animal and cellular models are necessary for the understanding of the neuropathology and the development of successful new therapeutic strategies. In this study we used the Ndufs4 knockout (Ndufs4(-/-)) mouse, a model of mitochondrial complex I deficiency. Ndusf4(-/-) mice exhibit progressive neurodegeneration, which closely resemble the human LD phenotype. When dissecting behavioral abnormalities in animal models it is of great importance to apply translational tools that are clinically relevant. To distinguish gait abnormalities in patients, simple walking tests can be assessed, but in animals this is not easy. This study is the first to demonstrate automated CatWalk gait analysis in the Ndufs4(-/-) mouse model. Marked differences were noted between Ndufs4(-/-) and control mice in dynamic, static, coordination and support parameters. Variation of walking speed was significantly increased in Ndufs4(-/-) mice, suggesting hampered and uncoordinated gait. Furthermore, decreased regularity index, increased base of support and changes in support were noted in the Ndufs4(-/-) mice. Here, we report the ability of the CatWalk system to sensitively assess gait abnormalities in Ndufs4(-/-) mice. This objective gait analysis can be of great value for intervention and drug efficacy studies in animal models for mitochondrial disease.