Diagnosis of PD and PD Progression Using DWI (K23)
|ClinicalTrials.gov Identifier: NCT02837172|
Recruitment Status : Completed
First Posted : July 19, 2016
Last Update Posted : June 19, 2019
|Condition or disease||Intervention/treatment|
|Parkinson's Disease||Other: Diffusion Weighted Imaging (DWI)|
Specific Aim 1a: Compare the outcome of a DTI based prediction with a contemporaneous clinical DAT scan in 100 subjects with suspected parkinsonism, and determine rate of concordance between the two diagnostic techniques.
Specific Aim 1b: Compare predictive accuracy of a baseline DTI with a "gold standard" expert diagnosis after 36 months of follow up in 100 subjects receiving DaTscan for suspected parkinsonism.
Specific Aim 2a: Use TBM to evaluate volume and cross-sectional caliber (based on point-wise fiber track direction) of the fimbria, pallidonigral tracts, and subthalamic-nigral tracts in PD and healthy controls. Ascertain if changes in white matter volume and caliber can be used to predict presence of PD from the PPMI study. Secondarily, using a model free approach, determine what white matter features based on TBM predict presence of disease.
Specific Aim 2b: Use TBM to determine if an increased rate of change in volume and cross-sectional caliber of the fimbria, and hypertrophic pallidonigral, and subthalamic-nigral tracts identified in aim 2a, are associated with a more rapid rate of disease progression in PD. Secondarily, using a model free approach, determine what white matter features based on TBM predict a faster rate of disease progression over the 5 year course of the PPMI study.
Specific Aim 3a: Compare DTI FA in TD-PD and PIGD-PD in the thalamus and lobule IX of the cerebellum , studying subjects from the PPMI study. Predict signal in these regions will predict phenotypic expression of disease. Using TBM and bootstrapping, determine the relationship between phenotypic expression of disease and white matter input/output pathways from the thalamus, and from lobule IX of the cerebellum.
|Study Type :||Observational|
|Actual Enrollment :||58 participants|
|Official Title:||Diagnosis of Parkinson's Disease and Prediction of Progression Using Diffusion Weighted Imaging|
|Actual Study Start Date :||September 25, 2014|
|Actual Primary Completion Date :||April 14, 2019|
|Actual Study Completion Date :||April 14, 2019|
Parkinson's disease from UAB
MDS-UPDRS,Montreal Cognitive Assessment, PDQ-39, Diffusion Weighted Imaging (DWI), and neurological examination.
Other: Diffusion Weighted Imaging (DWI)
MDS-UPDRS,Montreal Cognitive Assessment, PDQ-39, DTI imaging (MRI), and neurological examination.
Expert evaluation: Record review, PD Medical History and PD Family History Form, the Montreal Cognitive Assessment, PDQ-39. standard, full, neurological examination, and MDS-UPDRS
Parkinson's disease from PPMI dataset
Obtain retrospective and prospective de-identified data from the The Parkinson's Progression Markers Initiative (PPMI) dataset on Parkinson's disease (PD) subjects that have the following characteristics: within 2 years of diagnosis, positive DaTscan, and not (at study entry) on any PD related medication.
Controls from PPMI dataset
Obtain retrospective and prospective de-identified DTI imaging and data from the PPMI dataset
- MRI and DAT scan: Accuracy of diagnosis of Parkinson's disease in a clinically relevant population [ Time Frame: 3-5 years ]The study investigators will measure if MRI, specifically diffusion weighted imaging, can predict existence of Parkinson's disease. The study investigators will valuate if the derived MRI prediction matches or exceeds the accuracy of DATscan in detecting Parkinson's disease. The clinical/radiology reading of the DAT scan will determine the DAT scan diagnosis. The MRI scan diagnosis will be derived from statistical analysis of the full 5-dimensional brain DWI signal, as well as signals such as MRI T1 and resting fMRI signal. Methods of analysis will include using standard statistical techniques, the investigators published novel statistical techniques, and techniques such as Deep Learning and other artificial intelligence/learning algorithms.
- Can MRI profile risk for tremor and postural instability in PD [ Time Frame: 3-5 years ]The study investigators will measure if MRI, specifically diffusion weighted imaging, can predict at disease onset which individuals with Parkinson's disease are at risk of developing significant postural instability and gait dysfunction.The MRI scan prediction will be derived from statistical analysis of the full 5-dimensional brain DWI signal, as well as signals such as MRI T1 and resting fMRI signal. Methods of analysis will include using standard statistical techniques, the investigators published novel statistical techniques, and techniques such as Deep Learning and other artificial intelligence/learning algorithms.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02837172
|United States, Alabama|
|University of Alabama at Birmingham|
|Birmingham, Alabama, United States, 35233|
|Principal Investigator:||Frank Skidmore, MD||University of Alabama at Birmingham|