Study Objectives: Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies and eventually phenoconverts to overt neurodegenerative diseases including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). Associations of baseline resting-state electroencephalography (EEG) with phenoconversion have been reported. In this study, we aimed to develop machine learning models to predict phenoconversion time and subtype using baseline EEG features in patients with iRBD. Methods: At baseline, resting-state EEG and neurological assessments were performed on patients with iRBD. Calculated EEG features included spectral power, weighted phase lag index, and Shannon entropy. Three models were used for survival prediction, and four models were used for α-synucleinopathy subtype prediction. The models were externally validated using data from a different institution. Results: A total of 236 iRBD patients were followed up for up to 8 years (mean 3.5 years), and 31 patients converted to α-synucleinopathies (16 PD, 9 DLB, 6 MSA). The best model for survival prediction was the random survival forest model with an integrated Brier score of 0.114 and a concordance index of 0.775. The K-nearest neighbor model was the best model for subtype prediction with an area under the receiver operating characteristic curve of 0.901. Slowing of the EEG was an important feature for both models. Conclusions: Machine learning models using baseline EEG features can be used to predict phenoconversion time and its subtype in patients with iRBD. Further research including large sample data from many countries is needed to make a more robust model.

Theory of mind (ToM, the ability to attribute mental states to others) deficit is a frequent finding in neurodegenerative conditions, mediated by a diffuse brain network confirmed by ¹⁸F-FDG-PET and MR imaging, involving frontal, temporal and parietal areas. However, the role of hubs and spokes network regions in ToM performance, and their respective damage, is still unclear. To study this mechanism, we combined ToM testing with brain ¹⁸F-FDG-PET imaging in 25 subjects with mild cognitive impairment due to Alzheimer’s disease (MCI–AD), 24 subjects with the behavioral variant of frontotemporal dementia (bvFTD) and 40 controls. Regions included in the ToM network were divided into hubs and spokes based on their structural connectivity and distribution of hypometabolism. The hubs of the ToM network were identified in frontal regions in both bvFTD and MCI–AD patients. A mediation analysis revealed that the impact of spokes damage on ToM performance was mediated by the integrity of hubs (p < 0.001), while the impact of hubs damage on ToM performance was independent from the integrity of spokes (p < 0.001). Our findings support the theory that a key role is played by the hubs in ToM deficits, suggesting that hubs could represent a final common pathway leading from the damage of spoke regions to clinical deficits.

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. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Introduction: Idiopathic/isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is considered the prodromal stage of alpha‐synucleinopathies. Thus, iRBD patients are the ideal target for disease‐modifying therapy. The risk FActoRs PREdictive of phenoconversion in iRBD Italian STudy (FARPRESTO) is an ongoing Italian database aimed at identifying risk factors of phenoconversion, and eventually to ease clinical trial enrollment of well‐characterized subjects.Methods: Polysomnography‐confirmed iRBD patients were retrospectively and prospectively enrolled. Baseline harmonized clinical and nigrostriatal functioning data were collected at baseline. Nigrostriatal functioning was evaluated by dopamine transporter‐single‐photon emission computed tomography (DaT‐SPECT) and categorized with visual semi‐quantification. Longitudinal data were evaluated to assess phenoconversion. Cox regressions were applied to calculate hazard ratios.Results: 365 patients were enrolled, and 289 patients with follow‐up (age 67.7 ± 7.3 years, 237 males, mean follow‐up 40 ± 37 months) were included in this study. At follow‐up, 97 iRBD patients (33.6%) phenoconverted to an overt synucleinopathy. Older age, motor and cognitive impairment, constipation, urinary and sexual dysfunction, depression, and visual semi‐quantification of nigrostriatal functioning predicted phenoconversion. The remaining 268 patients are in follow‐up within the FARPRESTO project.Conclusions: Clinical data (older age, motor and cognitive impairment, constipation, urinary and sexual dysfunction, depression) predicted phenoconversion in this multicenter, longitudinal, observational study. A standardized visual approach for semi‐quantification of DaT‐SPECT is proposed as a practical risk factor for phenoconversion in iRBD patients. Of note, non‐converted and newly diagnosed iRBD patients, who represent a trial‐ready cohort for upcoming disease‐modification trials, are currently being enrolled and followed in the FARPRESTO study. New data are expected to allow better risk characterization.

Background: Cerebral amyloid angiopathy (CAA) is a heterogeneous small vessel disease that can occur independently or alongside Alzheimer disease (AD). CAA is diagnosed using the Boston Criteria 2.0, integrating clinical and neuroimaging features, whereas the Cerebrospinal Fluid (CSF) role in clinical practice remains under investigation. This study explores whether CSF biomarkers can identify distinct disease phenotypes, supporting hemorrhagic risk stratification. Methods: We enrolled probable patients with CAA retrospectively (Boston Criteria 2.0) from 2 institutions, collecting clinical, neuroimaging, and follow‐up data alongside core CSF biomarkers (Aβ40 [amyloid β 1–40], Aβ42 [amyloid β 1–42], p‐Tau181 [phosphorylated Tau], total‐Tau). Patients with CAA were stratified applying the Amyloid Tau Neurodegeneration (ATN) research framework, according to the presence of CSF amyloidosis (A ⁺ CAA versus A ⁻ CAA) and tauopathy (A ⁺ T ⁺ CAA versus A ⁺ T ⁻ CAA), and using unsupervised clustering, which defined CAA subgroups based on CSF biomarker levels only. Kaplan‐Meier and Cox regression analyses assessed the predictive value of CSF‐based subgroups for symptomatic hemorrhages during follow‐up. Results: Seventy‐one probable CAA patients (aged 71.77±8.45 years, 66% men, median follow‐up 1.15 years [0.50–2.44]) were enrolled. A ⁺ CAA showed a higher prevalence of cortical superficial siderosis than A ⁻ CAA (67% versus 25%, P =0.016). A ⁺ T ⁻ CAA had a greater hemorrhagic risk than A ⁺ T ⁺ CAA during follow‐up (29 versus 7 events per 100 patient‐years, P =0.010; log‐rank test: P =0.013). Unsupervised clustering identified 2 subgroups, which we defined as pure CAA and CAA‐ADA, with pure CAA presenting more symptomatic hemorrhages during follow‐up (22 versus 0 events per 100 patient‐years, P =0.017; log‐rank test, P =0.011). Conclusions: CSF‐based profiling effectively stratifies CAA phenotypes, offering a promising prognostic tool alongside neuroimaging markers. Further validation is needed to confirm its role in identifying patients with CAA with different natural histories.