Derivation and Validation of a Phenoconversion-Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder

Pietro Mattioli, Beatrice Orso, Claudio Liguori, Francesco Famà, Laura Giorgetti, Andrea Donniaquio, Federico Massa, Andrea Giberti, David Vállez García, Sanne K. Meles, Klaus L. Leenders, Fabio Placidi, Matteo Spanetta, Agostino Chiaravallotti, Riccardo Camedda, Orazio Schillaci, Francesca Izzi, Nicola B. Mercuri, Matteo Pardini, Matteo BaucknehtSilvia Morbelli, Flavio Nobili, Dario Arnaldi

Research output: Contribution to journalArticleAcademicpeer-review


Background: Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of α-synucleinopathies. Reliable biomarkers are needed to predict phenoconversion. Objective: The aim was to derive and validate a brain glucose metabolism pattern related to phenoconversion in iRBD (iRBDconvRP) using spatial covariance analysis (Scaled Subprofile Model and Principal Component Analysis [SSM-PCA]). Methods: Seventy-six consecutive iRBD patients (70 ± 6 years, 15 women) were enrolled in two centers and prospectively evaluated to assess phenoconversion (30 converters, 73 ± 6 years, 14 Parkinson's disease and 16 dementia with Lewy bodies, follow-up time: 21 ± 14 months; 46 nonconverters, 69 ± 6 years, follow-up time: 33 ± 19 months). All patients underwent [ 18F]FDG-PET ( 18F-fluorodeoxyglucose positron emitting tomography) to investigate brain glucose metabolism at baseline. SSM-PCA was applied to obtain the iRBDconvRP; nonconverter patients were considered as the reference group. Survival analysis and Cox regression were applied to explore prediction power. Results: First, we derived and validated two distinct center-specific iRBDconvRP that were comparable and significantly able to predict phenoconversion. Then, SSM-PCA was applied to the whole set, identifying the iRBDconvRP. The iRBDconvRP included positive voxel weights in cerebellum; brainstem; anterior cingulate cortex; lentiform nucleus; and middle, mesial temporal, and postcentral areas. Negative voxel weights were found in posterior cingulate, precuneus, middle frontal gyrus, and parietal areas. Receiver operating characteristic analysis showed an area under the curve of 0.85 (sensitivity: 87%, specificity: 72%), discriminating converters from nonconverters. The iRBDconvRP significantly predicted phenoconversion (hazard ratio: 7.42, 95% confidence interval: 2.6–21.4). Conclusions: We derived and validated an iRBDconvRP to efficiently discriminate converter from nonconverter iRBD patients. [ 18F]FDG-PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients.

Original languageEnglish
JournalMovement disorders
Early online date2022
Publication statusE-pub ahead of print - 2022


  • disease-related pattern
  • fluorodeoxyglucose positron emitting tomography
  • phenoconversion
  • rapid eye movement sleep behavior disorder
  • α-synucleinopathy

Cite this