Dynamic footprint based locomotion sway assessment in α-synucleinopathic mice using Fast Fourier Transform and Low Pass Filter
Timotius I, Canneva F, Minakaki G, Pasluosta CF, Moceri S, Casadei N, Riess O, Winkler J, Klucken J, von Hörsten S, Eskofier B (2018)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2018
Journal
Book Volume: 296
Pages Range: 1-11
URI: http://www.sciencedirect.com/science/article/pii/S0165027017304211
DOI: 10.1016/j.jneumeth.2017.12.004
Open Access Link: http://www.sciencedirect.com/science/article/pii/S0165027017304211
Abstract
Background
Sway is a crucial gait characteristic tightly correlated with the risk of falling in patients with Parkinson´s 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.
Authors with CRIS profile
Ivanna Timotius
Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)
Georgia Minakaki
Professur für Molekulare Neurologie
Cristian Federico Pasluosta
Sandra Moceri Präklinisches Experimentelles Tierzentrum (PETZ) Jürgen Winkler Professur für Molekulare Neurologie Jochen Klucken Department of Molecular Neurology Stephan von Hörsten Professur für Experimentelle Biomedizin Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)
Sandra Moceri Präklinisches Experimentelles Tierzentrum (PETZ) Jürgen Winkler Professur für Molekulare Neurologie Jochen Klucken Department of Molecular Neurology Stephan von Hörsten Professur für Experimentelle Biomedizin Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)
Additional Organisation(s)
Related research project(s)
Involved external institutions
Germany (DE)How to cite
APA:
Timotius, I., Canneva, F., Minakaki, G., Pasluosta, C.F., Moceri, S., Casadei, N.,... Eskofier, B. (2018). Dynamic footprint based locomotion sway assessment in α-synucleinopathic mice using Fast Fourier Transform and Low Pass Filter. Journal of Neuroscience Methods, 296, 1-11. https://doi.org/10.1016/j.jneumeth.2017.12.004
MLA:
Timotius, Ivanna, et al. "Dynamic footprint based locomotion sway assessment in α-synucleinopathic mice using Fast Fourier Transform and Low Pass Filter." Journal of Neuroscience Methods 296 (2018): 1-11.
BibTeX: Download