Poster E5 - Enrico Podesta-Quantifying postural stability in preclinical models of Parkinson’s disease

Thursday, October 24, 2024

2:45 PM - 3:45 PM EST

Location: Exhibit Hall E, F & G

Presenting Author(s)

Enrico Gabriel Podesta (he/him/his)

Research volunteer
University of Minnesota Twin Cities
Minneapolis, Minnesota, United States

Primary Investigator(s)

Matthew Johnson

Biomedical Engineering Faculty Member
University of Minnesota-Twin Cities, United States

Co-Author(s)

NP

Noeline Prins

Staff Scientist
University of Minnesota-Twin Cities, United States
Ming Lei (she/her/hers)

Mechancial Engineering doctoral student
University of Minnesota-Twin Cities, Minnesota, United States
BM

Biswarajan Mohanty

Research Staff
University of Minnesota-Twin Cities, United States
NH

Noah Hjelle

Research Staff
University of Minnesota-Twin Cities, United States

Introduction: In people with Parkinson’s disease (PD), difficulties with postural control, turning, and freezing of gait are often the most debilitating motor signs and the most challenging to treat with current forms of medication and surgical therapies. Towards the goal of developing and translating new therapies for these parkinsonian motor signs, it is important to be able to accurately quantify the degree of motor sign impairment objectively. Previous studies in humans have used force plates, marker(less) tracking systems, and gait mats to quantify these motor signs. The first two were not implemented since they lack spatial resolution and pressure information, respectively. Because the gait mats provide this useful information for biomechanical analysis, a sensor of this kind was used. In this study, we developed an integrated pressure mat system for quantifying features of postural stability, turning fluidity, and festination of gait during natural behaviors in a large preclinical animal model of PD. The system is easy to install and use, and invisible to animals, allowing the analysis of their normal gait without distractions.

Materials and

Methods: For this project, a 32x32 pressure sensor grid (RX-M3232L, 8.5x8.5 mm per sensor) was integrated with an Arduino Mega 2650, four 16-channel analog multiplexers, voltage divider circuits, and microcontroller code that ran the scan across the 1024 pressure mat sensors at a whole mat sample rate of 2.2 Hz. Sensors were calibrated individually prior to testing. Three cameras were used to observe gait patterns, as well. The system was then used to study postural control during static and turning behaviors in three non-human primates (two naive and one parkinsonian) with one evaluated on and off subthalamic nucleus deep brain stimulation therapy. A food or juice reward was offered to enter an enclosure with the pressure mat, and the step length before and after turning was calculated from the pressure mat data, and event markers were used to quantify turning parameters. Four behavioral sessions performed for each subject. Static behavior (i.e. resting) was classified by stances with durations >3 seconds.

Results, Conclusions, and Discussions: Results
The pressure mat data was capable of recording (1) turning time and (2) the number of steps per turn, as well as the number of passes through the enclosure, sway length in the x and y directions, span of sway, average velocity of sway in the x and y directions, step length upon entering enclosure, and step length after leaving the enclosure. As predicted, the parkinsonian condition was characterized by increased turning time (naive: 1.79±0.25s, 0.95±0.07s; parkinsonian: 11.51±1.69s), increased number of steps to turn (naive: 2.42±0.27, 2.39±0.10; parkinsonian: 5.5±0.50), and increased span of sway (naive: 38.43±6.40 mm, 57.54±5.74mm ; parkinsonian: 72.40±18.72mm). Deep brain stimulation in the parkinsonian subject decreased turning time (8.77±1.64s), the number of steps to turn (4±0.42), and span of sway (49.01±10.97 mm).

Conclusions
The pressure mat system was able to successfully stream locomotor data in the context of static posturing and turning movements, which were visually validated using markerless tracking of joint positions within the enclosure.

Discussion
The purpose of this study was to demonstrate feasibility for using this novel tool to quantify postural control in the context of static and dynamic tasks. Further study is needed to investigate if the specific features extracted from the pressure mat data are different between naive and parkinsonian conditions and between parkinsonian and therapeutic conditions (such as with deep brain stimulation).

Acknowledgements (Optional): This work was supported by the NIH-NINDS under grants R01-NS094206 and P50-NS123109. We thank the NMRC (Neuromodulation Research Center at the University of Minnesota) and in particular Biswa Mohanty and Jing Wang for assistance with data collection.