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

Journal article
(Original article)


Publication Details

Author(s): Timotius I, Canneva F, Minakaki G, Pasluosta CF, Moceri S, Casadei N, Riess O, Winkler J, Klucken J, von Hörsten S, Eskofier B
Journal: Journal of Neuroscience Methods
Publication year: 2018
Volume: 296
Pages range: 1-11
ISSN: 0165-0270
Language: English


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.



FAU Authors / FAU Editors

Eskofier, Björn Prof. Dr.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Klucken, Jochen Prof. Dr.
Molekular-Neurologische Abteilung in der Neurologischen Klinik
Minakaki, Georgia
Professur für Molekulare Neurologie
Moceri, Sandra
Core Unit Präklinisches Tierzentrum (CU-PTZ)
Pasluosta, Cristian Federico
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Timotius, Ivanna
Lehrstuhl für Informatik 5 (Mustererkennung)
von Hörsten, Stephan Prof. Dr.
Professur für Experimentelle Biomedizin
Winkler, Jürgen Prof. Dr.
Molekular-Neurologische Abteilung in der Neurologischen Klinik


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://dx.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: 

Last updated on 2018-19-04 at 04:30