Diagnosis of PD and PD Progression Using DWI

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Diagnosis of Parkinson's Disease and Prediction of Progression Using Diffusion Weighted Imaging

Conditions
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Parkinson's Disease

Intervention / Treatment
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* Other: Diffusion Weighted Imaging (DWI)


Participation Criteria
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Eligibility Criteria
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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)

Ages Eligible for Study
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Minimum Age: 19 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Parkinson's disease from UAB<br>MDS-UPDRS,Montreal Cognitive Assessment, PDQ-39, Diffusion Weighted Imaging (DWI), and neurological examination. | Other: Diffusion Weighted Imaging (DWI)<br>* 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<br>|
| Parkinson's disease from PPMI dataset<br>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<br>Obtain retrospective and prospective de-identified DTI imaging and data from the PPMI dataset | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| MRI and DAT scan: Accuracy of diagnosis of Parkinson's disease in a clinically relevant population | 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. | 3-5 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Can MRI profile risk for tremor and postural instability in PD | 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. | 3-5 years |