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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| NUK-RB Study | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| PET/CT with 18-FDG | Device | FDG-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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | From enrollment to end of follow-up period, expected to be 48 months |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | From enrollment to end of follow-up period, expected to be 48 months |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Axel Rominger, Prof. Dr. med. | Contact | +41 316322610 | axel.rominger@insel.ch | |
| Franziska Strunz, PhD | Contact | +41 316643022 | studies.nuk@insel.ch |
| Name | Affiliation | Role |
|---|---|---|
| Kuanggyu Shi, Prof. Dr. ing. | University Bern, Inselspital, Center for Artificial Intelligence in Medicine | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Inselspital, University Clinic for Nuclear Medicine | Recruiting | Bern | 3010 | Switzerland |
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| SPECT : 123 I-FP-CIT (DATSCAN) | Device | DaT-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 | Device | MRI examination to exclude structural brain anomalies. |
|
| 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. | From enrollment to end of follow-up period, expected to be 48 months |
| ID | Term |
|---|---|
| D010300 | Parkinson Disease |
| D020187 | REM Sleep Behavior Disorder |
| D020961 | Lewy Body Disease |
| ID | Term |
|---|---|
| D020734 | Parkinsonian Disorders |
| D001480 | Basal Ganglia Diseases |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D009069 | Movement Disorders |
| D000080874 | Synucleinopathies |
| D019636 | Neurodegenerative Diseases |
| D020923 | REM Sleep Parasomnias |
| D020447 | Parasomnias |
| D012893 | Sleep Wake Disorders |
| D001523 | Mental Disorders |
| D003704 | Dementia |
| D019965 | Neurocognitive Disorders |
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| ID | Term |
|---|---|
| D000072078 | Positron Emission Tomography Computed Tomography |
| ID | Term |
|---|---|
| D049268 | Positron-Emission Tomography |
| D014055 | Tomography, Emission-Computed |
| D007090 | Image Interpretation, Computer-Assisted |
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D014057 | Tomography, X-Ray Computed |
| D064847 | Multimodal Imaging |
| D011856 | Radiographic Image Enhancement |
| D007089 | Image Enhancement |
| D010781 | Photography |
| D011859 | Radiography |
| D014056 | Tomography, X-Ray |
| D011877 | Radionuclide Imaging |
| D014054 | Tomography |
| D003947 | Diagnostic Techniques, Radioisotope |
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