In collaboration with approximately 8 centers that specialize in iRBD we will recruit a total of 80 individuals for the study. All subjects will be enrolled into a 2-year longitudinal study where skin biopsies will be performed at 3 sites on each patient at 12-month intervals (baseline, year 1, year 2). Plasma blood collection will be performed at 12-month intervals (baseline, year 1, year 2). Detailed quantified examination, cognitive evaluation, medical history, and questionnaires will be performed at each visit. Additional biomarker, imaging and clinical information (if available) will be obtained for the purpose of determining phenoconversion to clinically apparent synucleinopathy. Subjects enrolled in the study will have baseline evaluations and follow up visits at 12 and 24 months to define any changes to clinical diagnosis (clinical phenoconversion). Skin biopsies will be repeated at the 12- and 24-month follow up visits to determine the rate of P-SYN accumulation over time and the rates of nerve fiber degeneration within punch skin biopsies.
Study Type
OBSERVATIONAL
Enrollment
80
Participating subjects will have three small skin punch biopsies.
MD First Research
Chandler, Arizona, United States
Banner Health
Phoenix, Arizona, United States
CND Life Sciences
Scottsdale, Arizona, United States
Cedars Sinai Medical Center
Los Angeles, California, United States
Stanford Neuroscience Health Center
Palo Alto, California, United States
University of Kentucky
Lexington, Kentucky, United States
Mount Sinai
New York, New York, United States
Texas Institute for Neurological Disorders
Sherman, Texas, United States
Advance the diagnostic utility of the Syn-One Test
Advance the diagnostic utility of the Syn-One Test™ by defining the metrics of P-SYN deposition and nerve fiber degeneration that predict phenoconversion in iRBD patients.
Time frame: 3 years
Enhance pathological reading through digital quantitative analysis of the Syn-One Test
Enhance pathological reading through digital quantitative analysis of the Syn-One Test™ using an AI-augmented detection system. Whole slide imaging with extraction of representative neural structures, target stain detection, segmentation, stain quantification and pattern recognition will be performed using deep learning algorithms. Results will be compared against pathologist readings and actual follow-up data to further refine model accuracy.
Time frame: 3 years
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