Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder

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Gaurav, Rahul | Pyatigorskaya, Nadya | Biondetti, Emma | Valabrègue, Romain | Yahia‐cherif, Lydia | Mangone, Graziella | Leu‐semenescu, Smaranda | Corvol, Jean‐christophe | Vidailhet, Marie | Arnulf, Isabelle | Lehéricy, Stéphane

Edité par CCSD ; Wiley -

International audience. Background: Isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of parkinsonism. Neurodegenerative changes in the substantia nigra pars compacta (SNc) in parkinsonism can be detected using neuromelanin-sensitive MRI. Objective: To investigate SNc neuromelanin changes in iRBD patients using fully automatic segmentation. Methods: We included 47 iRBD patients, 134 early Parkinson's disease (PD) patients and 55 healthy volunteers (HVs) scanned at 3 Tesla. SNc regions-of-interest were delineated automatically using convolutional neural network. SNc volumes, volumes corrected by total intracranial volume, signal-to-noise ratio (SNR) and contrastto-noise ratio were computed. One-way general linear models (GLM) analysis of covariance (ANCOVA) was conducted while adjusting for age and sex. Results: All SNc measurements differed significantly between the three groups (except SNR in iRBD). Changes in iRBD were intermediate between those in PD and HVs. Conclusions: Using fully automated SNc segmentation method and neuromelanin-sensitive imaging, iRBD patients showed neurodegenerative changes in the SNc at a lower level than in PD patients.

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