Dataset Information

Neurologic and psychiatric features of impending neurodegeneration in iRBD.

ABSTRACT:

Introduction

Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is linked to Parkinson's disease and other alpha-synucleinopathies, but various subsets of iRBD may not carry equal risk (i.e., those with depression are at higher risk than those without). Here, we prospectively focus on neurologic and psychiatric aspects of subjects with iRBD, in an attempt to determine what factors are prominent in those who undergo phenoconversion as opposed to those who do not.

Methods

We analyzed data from the "REM Sleep Behavior Disorder Associations with Parkinson's Disease Study (RAPiDS)" cohort both at baseline and then at follow-up evaluations (1 to 3 years later) utilizing several neurologic batteries, including the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS), the Montreal Cognitive Assessment (MoCA), the Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease (QUIP), the 10-M Walk Test (10MWT), and the Epworth Sleepiness Scale. Determination of phenoconversion was ascertained from physical examination and medical chart review from the initial evaluation onward.

Results

Of those who completed both evaluations, there were 33 subjects with iRBD, with an average age of 63.1 ± 12.8 years, with 9 women and 24 men. Of these, 8 (24%) iRBD subjects developed neurodegenerative illness, and demonstrated multiple areas of neurologic and psychiatric signs and symptoms, such as speech and movement problems as well as anxiety and depression.

Conclusions

Our data adds to the literature regarding risk of phenoconversion in those with iRBD. Further study will be needed, but it is clear that not all subjects with iRBD present the same risk for neurodegeneration.

SUBMITTER: Barone DA

PROVIDER: S-EPMC10480303 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Neurologic and psychiatric features of impending neurodegeneration in iRBD.

Barone Daniel A DA Sarva Harini H Hellmers Natalie N Wang Fei F Wu Zhenxing Z Krieger Ana C AC Henchcliffe Claire C

Clinical parkinsonism & related disorders 20230825

<h4>Introduction</h4>Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is linked to Parkinson's disease and other alpha-synucleinopathies, but various subsets of iRBD may not carry equal risk (i.e., those with depression are at higher risk than those without). Here, we prospectively focus on neurologic and psychiatric aspects of subjects with iRBD, in an attempt to determine what factors are prominent in those who undergo phenoconversion as opposed to those who do not.<h4>Method ...[more]

PMID: 37680305

Similar Datasets

Project description:EEG plays an integral part in the diagnosis and management of children with genetic epilepsies. Nevertheless, how quantitative EEG features differ between genetic epilepsies and neurological outcomes remains largely unknown. Here, we aimed to identify quantitative EEG biomarkers in children with epilepsy and a genetic diagnosis in STXBP1, SCN1A, or SYNGAP1, and to assess how quantitative EEG features associate with neurological outcomes in genetic epilepsies more broadly. We analyzed individuals with pathogenic variants in STXBP1 (95 EEGs, n=20), SCN1A (154 EEGs, n=68), and SYNGAP1 (46 EEGs, n=21) and a control cohort of individuals without epilepsy or known cerebral disease (847 EEGs, n=806). After removing artifacts and epochs with excess noise or altered state from EEGs, we extracted spectral features. We validated our preprocessing pipeline by comparing automatically-detected posterior dominant rhythm (PDR) to annotations from clinical EEG reports. Next, as a coarse measure of pathological slowing, we compared the alpha-delta bandpower ratio between controls and the different genetic epilepsies. We then trained random forest models to predict a diagnosis of STXBP1, SCN1A, and SYNGAP1. Finally, to understand how EEG features vary with neurological outcomes, we trained random forest models to predict seizure frequency and motor function. There was strong agreement between the automatically-calculated PDR and clinical EEG reports (R 2=0.75). Individuals with STXBP1-related epilepsy have a significantly lower alpha-delta ratio than controls (P<0.001) across all age groups. Additionally, individuals with a missense variant in STXBP1 have a significantly lower alpha-delta ratio than those with a protein-truncating variant in toddlers (P<0.001), children (P=0.02), and adults (P<0.001). Models accurately predicted a diagnosis of STXBP1 (AUC=0.91), SYNGAP1 (AUC=0.82), and SCN1A (AUC=0.86) against controls and from each other in a three-class model (accuracy=0.74). From these models, we isolated highly correlated biomarkers for these respective genetic disorders, including alpha-theta ratio in frontal, occipital, and parietal electrodes with STXBP1, SYNGAP1, and SCN1A, respectively. Models were unable to predict seizure frequency (AUC=0.53). Random forest models predicted motor scores significantly better than age-based null models (P<0.001), suggesting spectral features contain information pertinent to gross motor function. In summary, we demonstrate that STXBP1-, SYNGAP1-, and SCN1A-related epilepsies have distinct quantitative EEG signatures. Furthermore, EEG spectral features are predictive of some functional outcome measures in patients with genetic epilepsies. Large-scale retrospective quantitative analysis of clinical EEG has the potential to discover novel biomarkers and to quantify and track individuals' disease progression across development.

Dataset Information

Neurologic and psychiatric features of impending neurodegeneration in iRBD.

Introduction

Methods

Results

Conclusions

Publications

Neurologic and psychiatric features of impending neurodegeneration in iRBD.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets