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Bogachev M, Sinitca A, Grigarevichius K, Pyko N, Lyanova A, Tsygankova M, Davletshin E, Petrov K, Ageeva T, Pyko S, Kaplun D, Kayumov A, Mukhamedshina Y. Video-based marker-free tracking and multi-scale analysis of mouse locomotor activity and behavioral aspects in an open field arena: A perspective approach to the quantification of complex gait disturbances associated with Alzheimer's disease. Front Neuroinform 2023; 17:1101112. [PMID: 36817970 PMCID: PMC9932053 DOI: 10.3389/fninf.2023.1101112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction Complex gait disturbances represent one of the prominent manifestations of various neurophysiological conditions, including widespread neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Therefore, instrumental measurement techniques and automatic computerized analysis appears essential for the differential diagnostics, as well as for the assessment of treatment effectiveness from experimental animal models to clinical settings. Methods Here we present a marker-free instrumental approach to the analysis of gait disturbances in animal models. Our approach is based on the analysis of video recordings obtained with a camera placed underneath an open field arena with transparent floor using the DeeperCut algorithm capable of online tracking of individual animal body parts, such as the snout, the paws and the tail. The extracted trajectories of animal body parts are next analyzed using an original computerized methodology that relies upon a generalized scalable model based on fractional Brownian motion with parameters identified by detrended partial cross-correlation analysis. Results We have shown that in a mouse model representative movement patterns are characterized by two asymptotic regimes characterized by integrated 1/f noise at small scales and nearly random displacements at large scales separated by a single crossover. More detailed analysis of gait disturbances revealed that the detrended cross-correlations between the movements of the snout, paws and tail relative to the animal body midpoint exhibit statistically significant discrepancies in the Alzheimer's disease mouse model compared to the control group at scales around the location of the crossover. Discussion We expect that the proposed approach, due to its universality, robustness and clear physical interpretation, is a promising direction for the design of applied analysis tools for the diagnostics of various gait disturbances and behavioral aspects in animal models. We further believe that the suggested mathematical models could be relevant as a complementary tool in clinical diagnostics of various neurophysiological conditions associated with movement disorders.
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Affiliation(s)
- Mikhail Bogachev
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Aleksandr Sinitca
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Konstantin Grigarevichius
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Nikita Pyko
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Asya Lyanova
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Margarita Tsygankova
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Eldar Davletshin
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Konstantin Petrov
- FRC Kazan Scientific Center of RAS, Arbuzov Institute of Organic and Physical Chemistry, Kazan, Russia
| | - Tatyana Ageeva
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Svetlana Pyko
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Dmitrii Kaplun
- Centre for Digital Telecommunication Technologies, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia
| | - Airat Kayumov
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Yana Mukhamedshina
- Institute for Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
- Department of Histology, Cytology and Embryology, Kazan State Medical University, Kazan, Russia
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