Rodent models for gait network disorders in Parkinson's disease - a translational perspective

No Evidence of Sensory Neuropathy in a Traditional Mouse Model of Idiopathic Parkinson's Disease

Parkinson's disease (PD) is the second-most common neurodegenerative disorder worldwide and is diagnosed based on motor impairments. Non-motor symptoms are also well-recognised in this disorder, and peripheral neuropathy is a frequent but poorly appreciated non-motor sign. Studying how central and peripheral sensory systems are affected can contribute to the development of targeted therapies and deepen our understanding of the pathophysiology of PD. Although the cause of sporadic PD is unknown, chronic exposure to the pesticide rotenone in humans increases the risk of developing the disease. Here, we aimed to investigate whether peripheral neuropathy is present in a traditional model of PD. Mice receiving intrastriatal rotenone showed greatly reduced dopamine terminals in the striatum and a reduction in tyrosine hydroxylase-positive neurons in the Substantia nigra pars compacta and developed progressive motor impairments in hindlimb stepping and rotarod but no change in spontaneous activity. Interestingly, repeated testing using gold-standard protocols showed no change in gut motility, a well-known non-motor symptom of PD. Importantly, we did not observe any change in heat, cold, or touch sensitivity, again based upon repeated testing with well-validated protocols that were statistically well powered. Therefore, this traditional model fails to replicate PD, and our data again reiterate the importance of the periphery to the disorder.

Gait-related beta-gamma phase amplitude coupling in the subthalamic nucleus of parkinsonian patients

Analysis of coupling between the phases and amplitudes of neural oscillations has gained increasing attention as an important mechanism for large-scale brain network dynamics. In Parkinson's disease (PD), preliminary evidence indicates abnormal beta-phase coupling to gamma-amplitude in different brain areas, including the subthalamic nucleus (STN). We analyzed bilateral STN local field potentials (LFPs) in eight subjects with PD chronically implanted with deep brain stimulation electrodes during upright quiet standing and unperturbed walking. Phase-amplitude coupling (PAC) was computed using the Kullback-Liebler method, based on the modulation index. Neurophysiological recordings were correlated with clinical and kinematic measurements and individual molecular brain imaging studies ([123I]FP-CIT and single-photon emission computed tomography). We showed a dopamine-related increase in subthalamic beta-gamma PAC from standing to walking. Patients with poor PAC modulation and low PAC during walking spent significantly more time in the stance and double support phase of the gait cycle. Our results provide new insights into the subthalamic contribution to human gait and suggest cross-frequency coupling as a gateway mechanism to convey patient-specific information of motor control for human locomotion.

High-throughput gait acquisition system for freely moving mice

Normal and pathological locomotion can be discriminated by analyzing an animal's gait on a linear walkway. This step is labor intensive and introduces experimental bias due to the handling involved while placing and removing the animal between trials. We designed a system consisting of a runway embedded within a larger arena, which can be traversed ad libitum by unsupervised, freely moving mice, triggering the recording of short clips of locomotor activity. Multiple body parts were tracked using DeepLabCut and fed to an analysis pipeline (GaitGrapher) to extract gait metrics. We compared the results from unsupervised against the standard experimenter-supervised approach and found that gait parameters analyzed via the new approach were similar to a previously validated approach (Visual Gait Lab). These data show the utility of incorporating an unsupervised, automated, approach for collecting kinematic data for gait analysis.NEW & NOTEWORTHY The acquisition and analysis of walkway data is a time-consuming task. Here, we provide an unmonitored approach for collecting gait metrics that reduces the handling and stress of mice and saves time. A detailed pipeline is outlined that provides for the collection and analysis of data using an integrated suite of tools.