Electroencephalographic spectro-spatial covariance patterns related to phenoconversion in isolated rapid eye movement sleep behavior disorder and their longitudinal trajectories in α-synucleinopathies.

STUDY OBJECTIVES: This study aimed to identify electroencephalographic (EEG) spectro-spatial covariance patterns associated with phenoconversion in isolated rapid eye movement sleep behavior disorder (iRBD) patients and explore their longitudinal trajectories within α-synucleinopathies. METHODS: We assessed 47 participants, including 35 patients with iRBD and 12 healthy controls (HC), through baseline eye-closed resting EEGs. Patients with iRBD underwent follow-up EEG assessments and 18 patients with iRBD converted (12 to Parkinson's disease (PD), 6 to dementia with Lewy bodies [DLB]) during follow-up. We derived EEG spectro-spatial covariance patterns for PD-RBD and DLB-RBD from converters and HC. Correlations with motor and cognitive function, baseline distinctions among iRBD converters and nonconverters, and longitudinal trajectories were examined. RESULTS: At baseline, converters exhibited higher PD-RBD and DLB-RBD beta2 pattern scores compared to nonconverters (each area under curve [AUC] = 0.7751). The delta and alpha spatial patterns effectively distinguished both PD and DLB converters from HC, with the alpha pattern showing high discriminative power (AUC = 0.9097 for PD-RBD, 0.9306 for DLB-RBD). Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III scores correlated positively with PD-RBD and DLB-RBD delta patterns (Spearman's rho = 0.688, p = 0.00014; rho = 0.539, p = 0.0055, respectively), with age and sex as cofactors. Distinct trajectories emerged during follow-up among PD converters, DLB converters, and iRBD nonconverters. CONCLUSIONS: Unique EEG spectro-spatial patterns specific to PD-RBD and DLB-RBD offer potential as predictive markers for phenoconversion to α-synucleinopathies in iRBD.

EEG-based machine learning models for the prediction of phenoconversion time and subtype in isolated rapid eye movement sleep behavior disorder.

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.

Quantitative measurement of motor activity during sleep in isolated REM sleep behavior disorder patients using actigraphy before and after treatment with clonazepam.

STUDY OBJECTIVES: We conducted a prospective study to quantify motor activity during sleep measured by actigraphy before and after 3 months of treatment with clonazepam in patients with video-polysomnography (vPSG) confirmed isolated rapid eye movement (REM) sleep behavior disorder (iRBD). METHODS: The motor activity amount (MAA) and the motor activity block (MAB) during sleep were obtained from actigraphy. Then, we compared quantitative actigraphic measures with the results of the REM sleep behavior disorder questionnaire for the previous 3-month period (RBDQ-3M) and of the Clinical Global Impression-Improvement scale (CGI-I), and analyzed correlations between baseline vPSG measures and actigraphic measures. RESULTS: Twenty-three iRBD patients were included in the study. After medication treatment, large activity MAA dropped in 39% of patients, and the number of MABs decreased in 30% of patients when applying 50% reduction criteria. 52% of patients showed more than 50% improvement in either one. On the other hand, 43% of patients answered "much or very much improved" on the CGI-I, and RBDQ-3M was reduced by more than half in 35% of patients. However, there was no significant association between the subjective and objective measures. Phasic submental muscle activity during REM sleep was highly correlated with small activity MAA (Spearman's rho = 0.78, p < .001) while proximal and axial movements during REM sleep correlated with large activity MAA (rho = 0.47, p = .030 for proximal movements, rho = 0.47, p = .032 for axial movements). CONCLUSIONS: Our findings imply that quantifying motor activity during sleep using actigraphy can objectively assess therapeutic response in drug trials in patients with iRBD.

Characterization of attentional event-related potential from REM sleep behavior disorder patients based on explainable machine learning.

BACKGROUND AND OBJECTIVE: Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of neurodegeneration and is associated with cortical dysfunction. The purpose of this study was to investigate the spatiotemporal characteristics of cortical activities underlying impaired visuospatial attention in iRBD patients using an explainable machine-learning approach. METHODS: An algorithm based on a convolutional neural network (CNN) was devised to discriminate cortical current source activities of iRBD patients due to single-trial event-related potentials (ERPs), from those of normal controls. The ERPs from 16 iRBD patients and 19 age- and sex-matched normal controls were recorded while the subjects were performing visuospatial attentional task, and converted to two-dimensional images representing current source densities on flattened cortical surface. The CNN classifier was trained based on overall data, and then, a transfer learning approach was applied for the fine-tuning to each patient. RESULTS: The trained classifier yielded high classification accuracy. The critical features for the classification were determined by layer-wise relevance propagation, so that the spatiotemporal characteristics of cortical activities that were most relevant to cognitive impairment in iRBD were revealed. CONCLUSIONS: These results suggest that the recognized dysfunction in visuospatial attention of iRBD patients originates from neural activity impairment in relevant cortical regions and may contribute to the development of useful iRBD biomarkers based on neural activity.

Alerting network alteration in isolated rapid eye movement sleep behavior disorder patients with mild cognitive impairment.

OBJECTIVE: Mild cognitive impairment (MCI) was found in 30-50% of the isolated REM sleep behavior disorder (iRBD) patients. Furthermore, it is known that patients with Parkinson's disease have attention network defects. Given that iRBD is known to be the prodromal disease of α-synucleinopathies, our aim was to investigate whether there are attention network dysfunctions in iRBD patients following the presence of MCI. METHODS: 14 healthy controls, 48 iRBD patients, 24 with MCI and 24 without MCI, were included in this study. Attention network task (ANT) was used to assess alerting, orienting, and executive control networks. Event-related potentials (ERPs) and behavioral performances were recorded during the ANT. Parietal N1 and P3 components were analyzed to find effects of the three attention networks. RESULTS: IRBD patients without MCI showed neuropsychological, behavioral, and ERP results similar to those of healthy controls. On the other hand, iRBD patients with MCI showed a general decline in cognitive domains with no alerting effect (controls, p = 0.043; iRBD-noMCI, p = 0.014; iRBD-MCI, p = 0.130) while preserving orienting and executive control effect. Furthermore, iRBD patients with MCI had impairments in executive function and verbal memory domains, compared to iRBD patients without MCI. CONCLUSIONS: Our findings indicate that when cognition is reduced to MCI levels in iRBD patients, the attention network, especially the alerting component, is impaired. The attention network and cognition, on the other hand, can be preserved in iRBD patients due to the compensatory mechanism.

The adherence-associated Fdp fasciclin I domain protein of the biohydrogen producer Rhodobacter sphaeroides is regulated by the global Prr pathway.

Expression of fdp, encoding a fasciclin I domain protein important for adherence in the hydrogen-producing bacterium Rhodobacter sphaeroides, was investigated under a range of conditions to gain insights into optimization of adherence for immobilization strategies suitable for H2 production. The fdp promoter was linked to a lacZ reporter and expressed in wild type and in PRRB and PRRA mutant strains of the Prr regulatory pathway. Expression was significantly negatively regulated by Prr under all conditions of aerobiosis tested including anaerobic conditions (required for H2 production), and aerobically regardless of growth phase, growth medium complexity or composition, carbon source, heat and cold shock and dark/light conditions. Negative fdp regulation by Prr was reflected in cellular levels of translated Fdp protein. Since Prr is required directly for nitrogenase expression, we propose optimization of Fdp-based adherence in R. sphaeroides for immobilized biohydrogen production by inactivation of the PrrA binding site(s) upstream of fdp.