Ignite Creation Date:
2024-05-11 @ 8:31 AM
Last Modification Date:
2024-10-26 @ 3:29 PM
Study NCT ID:
NCT06405334
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-05-08
First Post:
2024-05-03
Brief Title:
Kinematic and Neural Dynamics of Postural Instability in Parkinsons Disease
Sponsor:
VA Office of Research and Development
Organization:
VA Office of Research and Development
Study Overview
Official Title:
Kinematic and Neural Dynamics of Postural Instability in Parkinsons Disease
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Balance problems and falls are among the most common complaints in Veterans with Parkinsons Disease PD but there are no effective treatments and the ability to measure balance and falls remains quite poor This study uses wearable sensors to measure balance and uses deep brain stimulation electrodes to measure electric signals from the brain in Veterans with PD The investigators hope to use this data to better understand the brain pathways underlying balance problems in PD so that new treatments to improve balance and reduce falls in Veterans with PD can be designed
Detailed Description:
Parkinsons disease PD is a progressive neurodegenerative disorder that manifests with the cardinal motor signs of bradykinesia rigidity tremor and postural instability PI Postural instability is a major cause of falls and as the disease process progresses the most common patient complaints center on gait and balance difficulties leading to falls Falls are the most common reason for hospitalization in PD patients impose a significant economic burden to the US healthcare system and are a major cause of diminished quality of life reduced mobility disability and death Both clinical and laboratory-based assessments of balance provide only a brief window of time into patients function Prior studies have demonstrated poor correlation between capacity based measurements in the clinic or lab ie what can a patient do when asked and performance-based measurements in the real world The investigators have developed methods to use wearable sensors in the ambulatory setting that can accurately detect a variety of activities and have created a number of quantitative metrics that are specific to PI In this manner the investigators can monitor and analyze PI in the real world ambulatory setting in Veterans with PD At present there are no effective long term treatments for PI Major impediments to progress in this field are an understanding of how patients actually experience PI at home and categorizing PI into meaningful phenotypic subtypes in order to understand its underlying pathophysiology and evaluate new treatments
The goal of this project is to better understand the underlying kinematic and electrophysiological components of postural instability in Veterans with PD Aim 1 sends PD patients home for one week with five wearable sensors and a neck-worn video camera to create a massive video-validated quantitative dataset of a variety of events that are relevant to analyzing PI at home walking turning sit to stand transitions near fallsstumbles For each video-validated event the investigators use deep learning algorithms to predict which activity occurred and create ROC curves to examine the algorithms predictive accuracy Aim 2 will use kinematic data obtained from the wearable sensors to develop deep clinical phenotypes of postural instability using principal component analysis PCA and unsupervised clustering machine learning methods Using these deep clinical phenotypes the investigators will then test specific hypotheses related to patients future fall risk their experience of PI at home and the relationship of these phenotypes to clinical data such as the presence of co-morbidities like peripheral neuropathy In Aim 3 a subset of Veterans with PD from the first two aims will undergo subthalamic nucleus STN DBS The investigators will use local field potential recordings from their leads to understand the physiological signatures that occur just prior to during and after a perturbation evoking a reactive postural response By recording from contacts in motor and associative regions while undergoing simultaneous kinematic recordings and associative STN stimulation the investigators can investigate the physiological basis of postural instability in these patients
The goal of this project is to better understand the underlying kinematic and electrophysiological components of postural instability in Veterans with PD Aim 1 sends PD patients home for one week with five wearable sensors and a neck-worn video camera to create a massive video-validated quantitative dataset of a variety of events that are relevant to analyzing PI at home walking turning sit to stand transitions near fallsstumbles For each video-validated event the investigators use deep learning algorithms to predict which activity occurred and create ROC curves to examine the algorithms predictive accuracy Aim 2 will use kinematic data obtained from the wearable sensors to develop deep clinical phenotypes of postural instability using principal component analysis PCA and unsupervised clustering machine learning methods Using these deep clinical phenotypes the investigators will then test specific hypotheses related to patients future fall risk their experience of PI at home and the relationship of these phenotypes to clinical data such as the presence of co-morbidities like peripheral neuropathy In Aim 3 a subset of Veterans with PD from the first two aims will undergo subthalamic nucleus STN DBS The investigators will use local field potential recordings from their leads to understand the physiological signatures that occur just prior to during and after a perturbation evoking a reactive postural response By recording from contacts in motor and associative regions while undergoing simultaneous kinematic recordings and associative STN stimulation the investigators can investigate the physiological basis of postural instability in these patients
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
None