Purpose. As the most frequent and earliest type of psychotic phenomenon in Parkinson’s disease (PD), minor hallucination (MH) can occur before the onset of motor symptoms. This sensation may be an early predictor of severe psychotic and cognitive states and is often overlooked in clinics. This study was aimed at providing a comprehensive and in-depth understanding of MHs. Patients and Methods. Demographic information was obtained from 262 patients with PD, and a series of clinical assessment questionnaires were provided. According to the result of the Movement Disorders Society Unified Parkinson’s Disease Rating Scale Part I, the patients were classified into the MH and nonhallucination (NH) groups. Results. MHs were the most common psychotic symptom with 38.9% prevalence. The most frequent MH was visual illusion, especially object misidentification. Three minor phenomena were somewhat consistent in terms of external factors, temporal factors, and content. Disease duration, daily levodopa equivalent dose, and percentage of levodopa and dopamine-receptor agonist use were remarkably greater in the MH group than in the NH group. After covariate control, the MH group had worse life quality and more severe nonmotor symptoms, including poor sleep quality and rapid eye movement sleep behavior disorder (RBD), than the NH group. The binary logistic regression model showed that RBD, sleep quality, and health-related life quality were associated with MHs. Conclusion. A high prevalence of MHs was observed in patients with PD. Further studies are needed to confirm and expand the identified clinical factors related to MH, which have potential prognostic and therapeutic implication.

Objective. Rapid eye movement (REM) sleep behavior disorder (RBD) is a common symptom in Parkinson’s disease (PD), and patients with PD-RBD tend to have an increased risk of cognitive decline and have the tendency to be akinetic/rigidity predominant. At the same time, the mechanisms of RBD in patients with PD remain unclear. Therefore, this study aimed to detect the structural and functional differences in patients with PD-RBD and PD without RBD (PD-nRBD). Methods. Twenty-four polysomnography-confirmed patients with PD-RBD, 26 patients with PD-nRBD, and 26 healthy controls were enrolled. Structural and functional patterns were analyzed based on voxel-based morphometry and seed-based functional connectivity (FC). Correlations between altered gray matter volume (GMV)/FC values and cognitive scores and motor impairment scores in PD subgroups were assessed. Results. Compared with patients with PD-nRBD, patients with PD-RBD showed relatively high GMV in the cerebellar vermis IV/V and low GMV in the right superior occipital gyrus (SOG). For the FC, patients with PD-RBD displayed lower FC between the right SOG and the posterior regions (left fusiform gyrus, left calcarine sulcus, and left superior parietal gyrus) compared with the patients with PD-nRBD. The GMV values in the right SOG were negatively correlated with the Unified PD Rating Scale-III scores in patients with PD-RBD but positively correlated with delayed memory scores. The GMV values in the cerebellar vermis IV/V were positively correlated with the tonic chin EMG density scores. There were positive correlations between the FC values in the right SOG-left superior parietal gyrus and MoCA and visuospatial skills/executive function scores and in the right SOG-left calcarine sulcus and delayed memory scores. Conclusion. Higher GMV in the cerebellum may be linked with the abnormal motor behaviors during REM sleep in patients with PD-RBD, and lower GMV and FC in the posterior regions may indicate that PD-RBD correspond to more serious neurodegeneration, especially the visuospatial–executive function impairment and delayed memory impairment. These findings provided new insights to learn more about the complicated characteristics in patients with PD-RBD.

The unavoidable muscle artifacts pose challenges on reliable interpretation of the electroencephalogram (EEG) recordings, especially for the wearable few-channel EEG, a new emerging scenario. However, the high computational load and low robustness of the existing methods limit its wider applications and performance in artifact removal. Consequently, we propose an efficient and robust muscle artifact removal approach by jointly employing the Fast Multivariate Empirical Mode Decomposition (FMEMD) and CCA for few-channel EEG. The proposed FMEMD-CCA firstly efficiently decomposes the input EEG recordings into several multivariate Intrinsic Mode Functions (IMF) by applying FMEMD. Secondly, all the multivariate IMFs are processed by CCA for computing the underlying sources. Finally, the sources with low autocorrelations are smartly determined as muscle artifacts and rejected, and therefore the other components are reconstructed for EMG-artifact-free IMFs and EEG. Simulated and real data experiments are carried out for verifying the performance of the proposed method. It takes 10 times less computing time in FMEMD-CCA compared with in Multivariate Empirical Mode Decomposition (MEMD)-CCA for 10-s EEG recordings, using the same computer and software. And the accuracy and the average correlation coefficient are highly consistent in both approaches. Furthermore, in contrast to MEMD-CCA, the proposed FMEMD-CCA works more robustly in low sampling rate based on the real data and benchmark.