Derek B Archer1, Justin T Bricker1, Winston T Chu1,2, Roxana G Burciu3, Johanna L Mccracken1, Song Lai4, Stephen A Coombes1, Ruogu Fang2, Angelos Barmpoutis2,5, Daniel M Corcos6, Ajay S Kurani7, Trina Mitchell1, Mieniecia L Black1, Ellen Herschel8, Tanya Simuni8, Todd B Parrish7, Cynthia Comella6, Tao Xie9, Klaus Seppi10, Nicolaas I Bohnen11,12,13,14, Martijn L T M Müller11,14, Roger L Albin12,13,14, Florian Krismer10, Guangwei Du15, Mechelle M Lewis15,16, Xuemei Huang15,16,17, Hong Li18, Ofer Pasternak19, Nikolaus R McFarland20,21, Michael S Okun20,21,22, David E Vaillancourt1,20,2,21. 1. Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL. 2. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL. 3. Department of Kinesiology and Applied Physiology, College of Health Sciences, University of Delaware, Newark, DE. 4. Department of Radiation Oncology & CTSI Human Imaging Core, University of Florida, Gainesville, FL. 5. Digital Worlds Institute, University of Florida, Gainesville, FL. 6. Department of Neurological Sciences, Rush University Medical Center, Chicago, IL. 7. Department of Radiology, Northwestern University, Chicago, IL. 8. Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois. 9. Department of Neurology, University of Chicago Medicine, Chicago, IL. 10. Department of Neurology, Neuroimaging Research Core Facility, Medical University Innsbruck, Innsbruck, Austria. 11. Department of Radiology, University of Michigan, Ann Arbor, MI. 12. Department of Neurology, University of Michigan, Ann Arbor, MI. 13. Neurology Service & Geriatrics Research, Education, and Clinical Center, VA Ann Arbor Healthcare. 14. The University of Michigan Morri K. Udall Center of Excellence for Parkinson's Disease Research, Ann Arbor, MI. 15. Department of Neurology, Penn State - Milton S. Hershey Medical Center, Hershey, PA. 16. Department of Pharmacology, Penn State - Milton S. Hershey Medical Center, Hershey, PA. 17. Departments of Neurosurgery, Radiology, and Kinesiology, Penn State - Milton S. Hershey Medical Center, Hershey, PA. 18. Department of Public Health Sciences, Medical College of South Carolina, Charleston, SC. 19. Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. 20. Fixel Institute for Neurological Disease, College of Medicine, University of Florida, Gainesville, FL. 21. Department of Neurology, University of Florida, McKnight Brain Institute, Gainesville, FL. 22. Department of Neurosurgery, University of Florida, McKnight Brain Institute, Gainesville, FL.
Abstract
Background: There is a critical need to develop valid, non-invasive biomarkers for Parkinsonian syndromes. The current 17-site, international study assesses whether non-invasive diffusion MRI (dMRI) can distinguish between Parkinsonian syndromes. Methods: We used dMRI from 1002 subjects, along with the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III), to develop and validate disease-specific machine learning comparisons using 60 template regions and tracts of interest in Montreal Neurological Institute (MNI) space between Parkinson's disease (PD) and Atypical Parkinsonism (multiple system atrophy - MSA, progressive supranuclear palsy - PSP), as well as between MSA and PSP. For each comparison, models were developed on a training/validation cohort and evaluated in a test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic (ROC) curves. Findings: In the test cohort for both disease-specific comparisons, AUCs were high in the dMRI + MDS-UPDRS (PD vs. Atypical Parkinsonism: 0·962; MSA vs. PSP: 0·897) and dMRI Only (PD vs. Atypical Parkinsonism: 0·955; MSA vs. PSP: 0·926) models, whereas the MDS-UPDRS III Only models had significantly lower AUCs (PD vs. Atypical Parkinsonism: 0·775; MSA vs. PSP: 0·582). Interpretations: This study provides an objective, validated, and generalizable imaging approach to distinguish different forms of Parkinsonian syndromes using multi-site dMRI cohorts. The dMRI method does not involve radioactive tracers, is completely automated, and can be collected in less than 12 minutes across 3T scanners worldwide. The use of this test could thus positively impact the clinical care of patients with Parkinson's disease and Parkinsonism as well as reduce the number of misdiagnosed cases in clinical trials.
Background: There is a critical need to develop valid, non-invasive biomarkers for Parkinsonian syndromes. The current 17-site, international study assesses whether non-invasive diffusion MRI (dMRI) can distinguish between Parkinsonian syndromes. Methods: We used dMRI from 1002 subjects, along with the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III), to develop and validate disease-specific machine learning comparisons using 60 template regions and tracts of interest in Montreal Neurological Institute (MNI) space between Parkinson's disease (PD) and Atypical Parkinsonism (multiple system atrophy - MSA, progressive supranuclear palsy - PSP), as well as between MSA and PSP. For each comparison, models were developed on a training/validation cohort and evaluated in a test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic (ROC) curves. Findings: In the test cohort for both disease-specific comparisons, AUCs were high in the dMRI + MDS-UPDRS (PD vs. Atypical Parkinsonism: 0·962; MSA vs. PSP: 0·897) and dMRI Only (PD vs. Atypical Parkinsonism: 0·955; MSA vs. PSP: 0·926) models, whereas the MDS-UPDRS III Only models had significantly lower AUCs (PD vs. Atypical Parkinsonism: 0·775; MSA vs. PSP: 0·582). Interpretations: This study provides an objective, validated, and generalizable imaging approach to distinguish different forms of Parkinsonian syndromes using multi-site dMRI cohorts. The dMRI method does not involve radioactive tracers, is completely automated, and can be collected in less than 12 minutes across 3T scanners worldwide. The use of this test could thus positively impact the clinical care of patients with Parkinson's disease and Parkinsonism as well as reduce the number of misdiagnosed cases in clinical trials.
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