Aljovic, Almir; Zhao, Shuqing; Chahin, Maryam; de la Rosa, Clara; Van Steenbergen, Valerie; Kerschensteiner, Martin; Bareyre, Florence M. ORCID: 0000-0002-0917-1725
(2022):
A deep learning-based toolbox for Automated Limb Motion Analysis (ALMA) in murine models of neurological disorders.
Communications Biology, 5: 131.
ISSN 2399-3642
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Abstract
In neuroscience research, the refined analysis of rodent locomotion is complex and cumbersome, and access to the technique is limited because of the necessity for expensive equipment. In this study, we implemented a new deep learning-based open-source toolbox for Automated Limb Motion Analysis (ALMA) that requires only basic behavioral equipment and an inexpensive camera. The ALMA toolbox enables the consistent and comprehensive analyses of locomotor kinematics and paw placement and can be applied to neurological conditions affecting the brain and spinal cord. We demonstrated that the ALMA toolbox can (1) robustly track the evolution of locomotor deficits after spinal cord injury, (2) sensitively detect locomotor abnormalities after traumatic brain injury, and (3) correctly predict disease onset in a multiple sclerosis model. We, therefore, established a broadly applicable automated and standardized approach that requires minimal financial and time commitments to facilitate the comprehensive analysis of locomotion in rodent disease models.
Doc-Type: | Article (LMU Hospital) |
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Organisational unit (Faculties): | 07 Medicine > Medical Center of the University of Munich > Neurological Clinic and Polyclinic with Friedrich Baur Institute |
DFG subject classification of scientific disciplines: | Life sciences |
Date Deposited: | 03. Nov 2022 10:28 |
Last Modified: | 07. Dec 2023 12:15 |
URI: | https://oa-fund.ub.uni-muenchen.de/id/eprint/299 |
DFG: | Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 491502892 |