Massa, Federico

Abstract Backgrounds Isolated REM sleep behaviour disorder (iRBD) is a prodromal marker of α-synucleinopathies. Although cognitive impairment is consistently reported in iRBD, its assessment varies considerably across studies. This study characterized mild cognitive impairment (MCI) patterns in iRBD using machine learning and identified the minimal neuropsychological test battery needed for accurate diagnosis. Methods Comprehensive neuropsychological data from 216 assessments (162 with MCI, 54 without) in 110 iRBD patients were analysed. Four supervised classifiers (logistic regression, support vector machine, random forest, gradient boosting) were trained across three frameworks (raw scores, raw scores plus demographics, binarized scores) to distinguish MCI status. A multi-objective hyperparameter optimization approach then identified the minimal test subset preserving diagnostic accuracy. Results Of MCI cases, 30% were single-domain and 70% multi-domain, with attention/executive functions and memory most frequently impaired. Binarized neuropsychological scores yielded the best classification performance, with support vector machine achieving 94.7% accuracy. Feature importance analyses consistently highlighted episodic memory, executive, and visuospatial measures. The optimization framework identified a mean of 8 informative tests spanning these domains, achieving comparable diagnostic accuracy to the full battery. Conclusions Cognitive impairment is common in iRBD and predominantly multidomain. Machine learning approaches applied to neuropsychological data can accurately classify MCI status and identify a concise, clinically feasible set of tests with high diagnostic value. These findings support comprehensive, multidomain neuropsychological assessment in iRBD and may inform optimized risk stratification in prodromal α-synucleinopathies.

Objective To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). Method s In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography‐confirmed RBD who eventually phenoconverted to overt alpha‐synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non‐phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. Results Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD‐converters from DLB‐converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z ‐score cutoff of −1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of −1.0 at the most affected hemisphere putamen/caudate ratio characterized PD‐converters. Interpretation Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease‐modifying trials. ANN NEUROL 2024;95:1178–1192

Abstract INTRODUCTION Sex influences neurodegeneration, but it has been poorly investigated in dementia with Lewy bodies (DLB). We investigated sex differences in brain atrophy in DLB using magnetic resonance imaging (MRI). METHODS We included 436 patients from the European‐DLB consortium and the Mayo Clinic. Sex differences and sex‐by‐age interactions were assessed through visual atrophy rating scales ( n = 327; 73 ± 8 years, 62% males) and automated estimations of regional gray matter volume and cortical thickness ( n = 165; 69 ± 9 years, 72% males). RESULTS We found a higher likelihood of frontal atrophy and smaller volumes in six cortical regions in males and thinner olfactory cortices in females. There were significant sex‐by‐age interactions in volume (six regions) and cortical thickness (seven regions) across the entire cortex. DISCUSSION We demonstrate that males have more widespread cortical atrophy at younger ages, but differences tend to disappear with increasing age, with males and females converging around the age of 75. Highlights Male DLB patients had higher odds for frontal atrophy on radiological visual rating scales. Male DLB patients displayed a widespread pattern of cortical gray matter alterations on automated methods. Sex differences in gray matter measures in DLB tended to disappear with increasing age.

Abstract Background and objectives. Sex is an important contributing factor to neuroimaging phenotypes in brain disorders. However, little is known about the contribution of sex differences to the neurodegeneration in dementia with Lewy bodies (DLB). We investigated sex differences in probable DLB patients by using both visual rating scales of lobar atrophy and automated estimations of regional atrophy. Methods. We included 442 probable DLB patients from the European-DLB consortium and the Mayo Clinic who have magnetic resonance imaging (MRI) data available. We assessed sex differences and the sex-by-age interaction in two largely independent samples through visual rating scales of lobar atrophy (n = 333; mean age 73 ± 8 years, 62% males) and automated regional estimations of gray matter (GM) volume and mean cortical thickness (CTh) (n = 165; mean age 69 ± 9 years, 72% males). We used binary logistic regression and ANOVA for statistical analysis. Results. We found a statistically significantly higher likelihood of frontal atrophy measured by the global cortical atrophy-frontal subscale (GCA-F) in males (40% of males had an abnormal GCA-F score versus 29% of females, P-value = 0.006). Using automated estimations, we found smaller GM volumes in 6 cortical regions in males compared with females, as well as smaller GM volume in the entorhinal cortex and thinner olfactory cortices in females, compared with males. The sex-by-age interaction showed statistically significant results in 6 cortical volumes and 7 mean CTh estimations (P-value ≤ 0.05), accentuated in the right middle frontal gyrus (FDR-adjusted P-value = 0.047). These cross-sectional interactions indicated that while females have statistically significantly less atrophy than males at younger ages, differences become non-significant at older ages, with females showing the same level of atrophy than males around the age of 75. Conclusions. This study demonstrates sex differences on brain atrophy in probable DLB. While male DLB patients have a more widespread pattern of cortical atrophy at younger ages, these sex differences tend to disappear with increasing age. Longitudinal studies will help establish these cross-sectional findings and inform on sex and age considerations to the use of MRI in clinical routine, as the field moves towards precision medicine.