Funding from The Parkinson Alliance helped to finance the following Parkinson’s research. Grantees were selected by scientific review committees of participating organizations. Updates will be posted, when available.
Project Title: Pilot Study Expansion—Walking While Talking: Effects of dual-task on gait and cognition in individuals with Parkinson’s disease
Principal Investigator(s): Lisa M. Muratori, PT, EdD
Background: This study is an expansion of a pilot study led by Lisa M. Muratori, PT, EdD, which was funded by a seed grant from Stony Brook’s Thomas Hartman Center for Parkinson’s Research. Additional funding will enable us to expand the study to include data from additional participants, which will be key to establishing sufficient preliminary data to advance the study’s objectives and seek NIH funding for a multi-center trial.
Objective/Rationale: Falls can be devastating for Parkinson’s patients, with 20-30% requiring hospitalization after a fall and a large percentage permanently losing their independence. For many PD patients, performing a simultaneous cognitive task such as talking while walking can negatively affect the speed and accuracy of their gait—potentially increasing fall risk while also reducing social participation and quality of life. Traditional physical therapy interventions often focus on the single task of walking, but there is a growing understanding that PT for individuals with PD must include dual-task training to maximize benefits. Our study aims to clarify the cognitive relationship between walking and talking, and develop new dual- and multi-task interventions that will directly benefit Parkinson’s patients.
Project Description/Methods/Design: Our study will build on previously-funded pilot data to further determine whether an innovative game-based treadmill intervention will improve walking speed, decrease gait deviations, improve measures of functional mobility, improve verbal fluency, etc. for individuals with PD. Participants will complete standardized questionnaires measuring quality of life and fear of falling, plus 16 physical therapy testing/training sessions (30 minutes each, 3 times per week for 4 weeks). During the sessions, participants will walk on a treadmill with and without the support of a specialized safety harness. While walking, individuals will participate in a proprietary video-based immersive game that will interact with their treadmill and increase in difficulty each week. During each session, vital signs will be recorded and specific data will be collected using standardized tests including: Functional Reach test, 6-Minute Walk test, Timed Up and Go test, Stops Talking When Walking (STWW) test, and Controlled Oral Word Association (COWA) test. Movements will also be recorded using a state-of-the-art motion analysis system to determine how temporal and spatial characteristics of gait are altered. Using this information, we will develop new multi-task physical therapy interventions with applications beyond treadmill training—to improve over-ground walking, functional mobility, and social participation for Parkinson’s patients.
Relevance to Treatment of Parkinson’s Disease: In order to ensure full social participation and maintain optimal quality of life, while reducing fall risk, individuals with neurological disease must be able to safely move while engaging in decision making, social interactions, and other cognitive tasks. This study will lead to better physical therapy interventions to increase simultaneous motor and cognitive function for patients living with PD.
Expected Outcome: We expect our study to improve the scientific community’s understanding of the basis of cognitive and motor disturbances as they relate to PD. Testing will result in readily accessible and practical interventions for rehabilitation professionals working with PD patients, as well as preliminary data to pursue a larger clinical study.
Project Title: Computational Modeling of 60 Hz Subthalamic Nucleus Deep Brain Stimulation for Gait Disorder in Parkinson’s disease
Principal Investigator: Ritesh Ramdhani, MD
Objective/Rationale: The goal of this study is to further our understanding and application of 60Hz subthalamic deep brain stimulation (STN-DBS) in Parkinson’s patients with gait disorder. We and others have published studies showing low-frequency STN stimulation (60-80Hz) in chronic DBS patients may alleviate freezing and improve gait.
Project Description/Methods/Design: We will assess the degree to which stimulation frequency parameters affect gait. The study will be conducted in two phases: The primary goal of Phase I is to establish a baseline movement profile of Parkinson’s subjects with DBS using motion sensors. The goal of Phase II is to investigate the impact of stimulation frequencies (60Hz and high) on gait characteristics. Each subject will be evaluated during an in-laboratory session for both phases under two conditions: 1) the practically defined OFF state following overnight withdrawal of their dopaminergic medications; and 2) levodopa ON State. We will then develop machine learning models to predict optimal subthalamic deep brain stimulation frequency.
Relevance to Treatment of Parkinson’s Disease: Gait disorder, which manifests as shuffling, reduction in speed, multistep turning, and/or freezing of gait (FOG), can arise later in the Parkinson’s disease (PD) course and cause significant disability. Ultimately, patients are at risk for falls and can become socially isolated due to their mobility limitations. These symptoms tend not to respond to high-frequency STN-DBS. However, lower frequency stimulation (60-80Hz) of the STN in treating gait disorder and/or freezing of gait has demonstrated benefit. This study potentially can expand knowledge of 60hz DBS while improving its utilization in combination with PD medications—enabling sustainable and possibly predictable therapeutic benefit.
Expected Outcome: This study will expand our understanding of DBS’s impact on gait problems in Parkinson’s patient. We expect that there will be a subset of patients who will benefit from 60Hz stimulation and that this cohort can be predicted from data-driven models.