Understanding Balance Problems in Parkinson's Disease
This observational study is looking at balance problems and falls in Veterans with Parkinson's Disease (PD). Researchers are using wearable sensors to measure balance and also looking at electrical signals from the brain using deep brain stimulation electrodes. The goal is to better understand the brain pathways that cause balance issues in PD. This knowledge could help design new treatments to improve balance and reduce falls. The study aims to enroll 100 participants. To join, you must be a Veteran with a clinical diagnosis of Parkinson's disease, in Hoehn and Yahr stage 2-3, and able to give informed consent. Success will be measured by identifying specific patterns in balance and brain activity over 4 years. The current recruitment status is unclear.
- Study design
- This is an observational study planning to enroll 100 participants. It is not specified if it is randomized, blinded, or what phase it is.
- What's involved
- Not specified in the trial record.
- Compensation
- Not stated in the trial record.
- Follow-up
- Participants will be followed for 4 years to measure balance and brain activity.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Kinematic and Neural Dynamics of Postural Instability in Parkinson's Disease
At a glance
Conditions
NCT06405334
Where you'd take part
This study runs at 1 site. They're the same protocol — you choose where, and that choice sets who your contact draft is addressed to.
Minneapolis VA Health Care System, Minneapolis, MN
Minneapolis, Minnesotastudy coordinator listed
Recruiting
Sites open and close at different times, so the status above is per site — it can differ from the study's overall status.
Study leadership
- Robert A McGovern · PRINCIPAL_INVESTIGATOR · Minneapolis VA Health Care System, Minneapolis, MN
Who to contact
Opens a ready-to-send draft in your own email app — review before sending.
Want this trial checked against your situation?
Add a private profile and we'll compare every criterion below against your situation — and tell you which ones are met, uncertain, or excluding.
Inclusion
Exclusion
What this trial measures
- Event Identification Receiver Operating Characteristic Curve4 years
All events are validated using the video camera recording. Using the CNN-LSTM algorithm, the investigators create a series of predicted events for the entire dataset of wearable sensor usage. For each event type, the investigators will then compare the predicted events based on this CNN-LSTM algorithm to the actual validated events. This will also allow us to assess the sensitivity, specificity, positive predictive value and negative predictive value for each event type. Finally, the investigators will create separate receiver operating characteristic (ROC) curves and calculate the AUC for each event type.
- Silhouette Scores4 years
All kinematic variables are first standardized using z-score conversion and then transformed into a new set of uncorrelated principal components that retain the original data's variation. Following dimensionality reduction with PCA, the investigators utilize k-means clustering to identify potential subgroups. K-means clustering partitions the data into distinct, non-overlapping subgroups based on minimizing within-cluster variance, with the optimal number of clusters determined through the elbow method. While k-means is the most common method and has been effective thus far, small sample size datasets typically fare better using hierarchical clustering. This method, conversely, constructs a hierarchy of clusters by iteratively combining the most similar clusters. Silhouette scores are calculated to assess how well separated the clusters are from each other.
- Associative STN alpha band power4 years
Associative and motor STN will be parcellated and the DBS lead reconstructed based on our prior work. The investigators will then use bipolar LFP recordings from the appropriate contact pairs to construct time frequency histograms and examine the event-related modulation of power in the response preparation, movement execution and post-movement execution phases of the postural response.
- Postural step length response during associative STN vs. motor STN stimulation vs. no stimulation4 years
The investigators will assess changes in reactive postural response kinematics to associative vs. motor vs. no STN stimulation to test whether any stimulation or stimulation location can improve PI. Linear mixed-effects models are used to test for within-patient changes in pull test kinematic parameters between groups. These models are adjusted for pull intensity, and baseline step length values. Models use a Bonferroni p-value correction to account for multiple testing. The investigators have previously been able to determine within-patient kinematic differences using our variable pull test method in a sample size of 13 movement disorder patients. With \~15 pull test trials for each condition, the investigators can demonstrate within-patient kinematic differences of about 5 cm in initial step length and 100 ms in reaction time.