Working…
COVID-19 is an emerging, rapidly evolving situation.
Get the latest public health information from CDC: https://www.coronavirus.gov.

Get the latest research information from NIH: https://www.nih.gov/coronavirus.
ClinicalTrials.gov
ClinicalTrials.gov Menu

Diagnosis of PD and PD Progression Using DWI (K23)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT02837172
Recruitment Status : Completed
First Posted : July 19, 2016
Last Update Posted : June 19, 2019
Sponsor:
Information provided by (Responsible Party):
Dr. Frank Michael Skidmore, University of Alabama at Birmingham

Brief Summary:
This project will evaluate the utility of diffusion tensor imaging (DTI) as an adjunctive method to improve early diagnosis of Parkinson's disease (PD). Two populations will be evaluated in this study: 1) Individuals with uncertain PD diagnosis who receive a DaTscan, and 2) individuals with well characterized PD and healthy controls, drawn from the fully enrolled Parkinson's Progression Markers Initiative (PPMI) PD and control cohorts.

Condition or disease Intervention/treatment
Parkinson's Disease Other: Diffusion Weighted Imaging (DWI)

Detailed Description:

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.

Layout table for study information
Study Type : Observational
Actual Enrollment : 58 participants
Observational Model: Case-Control
Time Perspective: Other
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

Resource links provided by the National Library of Medicine


Group/Cohort Intervention/treatment
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



Primary Outcome Measures :
  1. 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.


Secondary Outcome Measures :
  1. 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.



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   19 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
100 PD subjects with DaTscan, and 210 (140 PD/70 control) from the PPMI dataset
Criteria

Inclusion Criteria:

  • Patients 19 and older
  • Referred for clinical DaTscan for possible PD
  • Controls from the PPMI dataset.

Exclusion Criteria:

  • Pregnant women
  • Participants that cannot participate in MRI (metallic artifact or other contraindication(s) to MRI at 3T)

Information from the National Library of Medicine

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


Locations
Layout table for location information
United States, Alabama
University of Alabama at Birmingham
Birmingham, Alabama, United States, 35233
Sponsors and Collaborators
University of Alabama at Birmingham
Investigators
Layout table for investigator information
Principal Investigator: Frank Skidmore, MD University of Alabama at Birmingham
Layout table for additonal information
Responsible Party: Dr. Frank Michael Skidmore, Assistant Professor of Neurology, University of Alabama at Birmingham
ClinicalTrials.gov Identifier: NCT02837172    
Other Study ID Numbers: K23NS083620 ( U.S. NIH Grant/Contract )
First Posted: July 19, 2016    Key Record Dates
Last Update Posted: June 19, 2019
Last Verified: June 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: progress report information to NIH
Additional relevant MeSH terms:
Layout table for MeSH terms
Parkinson Disease
Parkinsonian Disorders
Basal Ganglia Diseases
Brain Diseases
Central Nervous System Diseases
Nervous System Diseases
Movement Disorders
Neurodegenerative Diseases