The study aims to systematically document the course of REM sleep behavior disorder (RBD) and investigate possible clinical and imaging biomarkers for disease progression and conversion risk to Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). The study will use artificial intelligence to analyze imaging and develop a reliable method to predict and stratify patients approaching conversion to overt a-synucleinopathy. Participants will be clinically evaluated and 2 imaging procedures will be done.
Confirmed clinical iRBD diagnosis by movement disorder specialists according to the International Classification of Sleep Disorders
Written informed consent
Known diagnosis of PD or other neurodegenerative disorder
Unequivocal signs of parkinsonism on examination
Narcolepsy or other known causes of RBD
Moderate to severe obstructive sleep apnea
Single group
PET/CT with 18-FDG
DeviceFDG-PET scans will be acquired in a Siemens Biograph Vision Quadra PET/CT (Siemens, Germany) at 30-minute post-injection of approximately 80 MBq 18F-FDG. The duration of the acquisition is 20 minutes. The PET images will be reconstructed with the vendor's time of flight (TOF) point-spread-function (PSF) algorithm, following corrections for randoms, scatter, and decay. Attenuation correction will be performed first using low-dose CT.
SPECT : 123 I-FP-CIT (DATSCAN)
DeviceDaT-Scans will be acquired in a GE Discovery NM/CT 670 Pro™. After injection of approximately 110 MBq 123I-FP-CIT, images will be acquired within 4 h post-injection. The duration of the acquisition is 35 minutes.
MRI
DeviceMRI examination to exclude structural brain anomalies.
Assessment of Deep Learning Model Accuracy in Predicting Neurodegenerative Conversion in isolated REM sleep behavior disorder (iRBD) through Early Biomarker Detection
The investigators aim to evaluate the predictive accuracy of a deep learning model in identifying patients with iRBD who will progress to a neurodegenerative disorder. The primary outcome will assess the model's sensitivity in detecting early imaging biomarkers linked to disease progression, with the goal of enabling earlier intervention and improving long-term outcomes.
Comparison of the Estimated versus Observed Annual Conversion Risk of Isolated Rapid Eye Movement Behavior Disorder (iRBD) to Neurodegenerative Disorders
The investigators aim to compare the estimated annual conversion risk of 6.3% in patients with iRBD to Parkinson's disease or another overt alpha-synucleinopathy with the conversion rates observed in the study.
Evaluation of Deep Learning Model Accuracy in Predicting Conversion of Isolated REM Sleep Behavior Disorder (iRBD) to Parkinson's Disease
The investigators aim to evaluate the accuracy, receiver operating characteristic curves and area under the curve, specificity, and positive and negative predictive values of the applied deep learning method, predicting the conversion risk from iRBD to Parkinson's disease or another overt alpha-synucleinopathy.
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