Research reportHigh-speed video gait analysis reveals early and characteristic locomotor phenotypes in mouse models of neurodegenerative movement disorders
Graphical abstract
Introduction
Neurodegenerative disorders of the central nervous system, depending on the structures involved, often affect movement and gait. In amyotrophic lateral sclerosis (ALS) muscle atrophy, paralysis and spasticity, due to spinal cord and motor cortex pathology, result in gait disturbances [1]. Damage to basal ganglia structures in Huntington’s disease (HD) alternatively causes a hyperkinetic disorder (chorea) combined with a loss of voluntary movements (bradykinesia and rigidity) [2]. Disorders primarily affecting cerebellar structures, such as spinocerebellar ataxias, result in impaired balance and coordination (ataxic gait) [3].
To better understand the underlying pathophysiological mechanisms, several mouse models representing these neurodegenerative disorders have been established: (1) SOD1 G93A transgenic (tg) mice demonstrate ALS-like motor neuron loss and hind-limb paralysis [4]; (2) Huntingtin N171-82Q mice develop abnormal gait and behavioral abnormalities [5]; (3) Harlequin mutants bear a mutation leading to cerebellar degeneration and ataxic syndrome [6].
Comprehensive analysis of motor and gait function is a cornerstone for the pre-clinical development of new therapies. Assessment techniques based on behavioral tests such as rotarod, grip strength or scoring systems are characterized by several limitations, including a restriction to a few specific aspects of locomotion, non-physiologic test conditions and a dependency on users and motivational factors. They often fail to fully capture delayed onset following treatment intervention (sensitivity) and progressive motor dysfunction (qualitative and quantitative changes). Therefore, more sophisticated techniques were needed.
Gait analysis is promising for the evaluation of motor deficits, as gait is a fundamental, physiological and unforced form of locomotion with direct clinical relevance. However, available systems focusing on ventral plane video gait analysis produce results that vary widely, fail to reproduce or even contradict previous findings [7], [8], [9], [10], [11], [12]. This is not surprising, as many characteristic changes in limb positioning and movement dynamics in ALS and HD models can only be observed in the lateral plane. Lately, innovative systems including machine learning algorithms (NeuroCube®) and analysis of the lateral view (MotoRater, Locomouse) became available and were used to examine gait more thoroughly [13], [14], [15], [16], [17]. But the maximum potential of lateral plane videography has not yet been exploited, as analyses were restricted to single functional aspects of gait and a limited number of gait parameters in few models.
In this study we performed a comprehensive whole-body locomotion and gait analysis of the mentioned mouse models of neurodegenerative movement disorders using the MotoRater (TSE-Systems, Bad Homburg, Germany), a high-speed video tracking system [18]. This system films the spontaneous gait from three sides (ventral and lateral planes) to track bony landmarks.
We followed an exploratory approach (using 17 tracking points) to study the movement of limbs, trunk and tail in order to create a highly comprehensive locomotor profile and developed a custom-built algorithm, which converts and interprets the raw tracking data and calculates 164 objective spatial and spatiotemporal parameters describing locomotion. Based on this extensive dataset, we identified a small set of relevant tracking points and parameters for each model, which specifically describe gait disturbances, and may be used in following studies to assess potential therapeutic approaches with a higher throughput. Further, we examined effects of locomotion speed and body weight, as previous gait analysis studies pointed out their influence on many individual locomotor parameters [19], [20], [21], [16]. Moreover, we compared the three models representing different human neurodegenerative gait disorders and observed specific and distinct motor profiles. Several gait changes observed in the three models correspond closely to the classical clinical symptoms described in these disorders.
Lastly, a higher sensitivity of this system compared to conventional motor tests was proven, revealing substantial locomotor deficits detectable prior to the onset of clinical impairments on conventional tasks.
Section snippets
Animals
All animal experiments were approved by the veterinarian authorities of the Canton of Zurich (animal license numbers: 109/2010, 183/2012, 229/2013), in compliance with Swiss law (“455.163 Tierversuchsverordnung” 2010) and international guidelines for care and use of animals.
All animals were held and housed 3–5 per cage under specific pathogen-free conditions in an air-conditioned and temperature controlled environment (22 °C ± 1 °C). 3 weeks after birth, tg animals were identified by standard PCR
Comprehensive locomotor profile describes gait in great detail
Our custom-built algorithm allowed the calculation of a comprehensive locomotor profile. This profile consists of 164 objective spatial and spatiotemporal parameters describing movement of every tracking point. For a detailed representation of all computed parameters see Supplementary Table S1. We identified a set of 25 parameters, of which each reflects specific gait deficiencies in one or more models, shown in Table 1. Parameters were classified into following three functional groups: (1)
Discussion
The examination of unforced locomotion has direct clinical relevance, as it is fundamental and physiologic. Unlike ventral plane approaches, whole-body video gait analysis is not restricted to paw placements and coordination, but provides a global assessment of locomotion and motor abilities. Consequently, our MotoRater analysis identified specific gait disturbances for each of the three different mouse models analyzed.
Mainly altered hind-limb movement, accompanied by some changes in
Conflict of interest
The authors have no conflicts of interest to disclose.
Acknowledgements
The authors would like to thank Anna Jeske, Belinda Ries and Stefan Preisig for excellent technical assistance and contributions to the project as well as Kristina Hersch for useful discussion and comments.
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