Clinical implication of high-density EEG sleep recordings in Parkinson’s disease

Macro and micro-sleep dysfunctions as translational biomarkers for Parkinson's disease

Sleep disturbances are highly prevalent among patients with Parkinson's disease (PD) and often appear from the early-phase disease or prodromal stages. In this chapter, we will discuss the current evidence addressing the links between sleep dysfunctions in PD, focusing most closely on those data from animal and mathematical/computational models, as well as in human-based studies that explore the electrophysiological and molecular mechanisms by which PD and sleep may be intertwined, whether as predictors or consequences of the disease. It is possible to clearly state that leucine-rich repeat kinase 2 gene (LRRK2) is significantly related to alterations in sleep architecture, particularly affecting rapid eye movement (REM) sleep and non-REM sleep, thus impacting sleep quality. Also, decreases in gamma power, observed after dopaminergic lesions, correlates negatively with the degree of injury, which brings other levels of understanding the impacts of the disease. Besides, abnormal synchronized oscillations among basal ganglia nuclei can be detrimental for information processing considering both motor and sleep-related processes. Altogether, despite clear advances in the field, it is still difficult to definitely establish a comprehensive understanding of causality among all the sleep dysfunctions with the disease itself. Although, certainly, the search for biomarkers is helping in shortening this road towards a better and faster diagnosis, as well as looking for more efficient treatments.

Auditory deep sleep stimulation in older adults at home: a randomized crossover trial

Background

Auditory stimulation has emerged as a promising tool to enhance non-invasively sleep slow waves, deep sleep brain oscillations that are tightly linked to sleep restoration and are diminished with age. While auditory stimulation showed a beneficial effect in lab-based studies, it remains unclear whether this stimulation approach could translate to real-life settings.

Methods

We present a fully remote, randomized, cross-over trial in healthy adults aged 62-78 years (clinicaltrials.gov: NCT03420677). We assessed slow wave activity as the primary outcome and sleep architecture and daily functions, e.g., vigilance and mood as secondary outcomes, after a two-week mobile auditory slow wave stimulation period and a two-week Sham period, interleaved with a two-week washout period. Participants were randomized in terms of which intervention condition will take place first using a blocked design to guarantee balance. Participants and experimenters performing the assessments were blinded to the condition.

Results

Out of 33 enrolled and screened participants, we report data of 16 participants that received identical intervention. We demonstrate a robust and significant enhancement of slow wave activity on the group-level based on two different auditory stimulation approaches with minor effects on sleep architecture and daily functions. We further highlight the existence of pronounced inter- and intra-individual differences in the slow wave response to auditory stimulation and establish predictions thereof.

Conclusions

While slow wave enhancement in healthy older adults is possible in fully remote settings, pronounced inter-individual differences in the response to auditory stimulation exist. Novel personalization solutions are needed to address these differences and our findings will guide future designs to effectively deliver auditory sleep stimulations using wearable technology.

Trends of complications in patients with Parkinson's disease in seven major cities of China from 2016 to 2019

Parkinson's disease (PD) is a neurological disorder involving both motor and nonmotor symptoms. Multimorbidity acts synergistically to heighten the risk of adverse outcomes for patients with PD. Its complications have a major impact on the clinical management of PD. The present retrospective and multicenter study was first performed to describe the epidemiological characteristics of PD patients and assess the incidence of complications. The outpatient prescriptions for PD therapy were collected from hospitals in Beijing, Chengdu, Guangzhou, Hangzhou, Shanghai, Tianjin and Zhengzhou of China over a 40-day period per year, from the first half of 2016 to that of 2019. The survey covered the characteristics and representative complications of the study population. A total of 103 674 outpatient prescriptions for PD treatment from different graded hospitals of China were collected for final data analysis. It showed that 78.15% of PD patients were prescribed in the neurology department. 95.05% of the outpatient prescriptions were from general hospitals. We found that the overall PD prevalence was 0.47%, among which 52.96% of them were men. In addition, 82.10% of PD suffers were older than 60 years and 83.70% of them had complications. The top five highest frequencies of nonmotor complications in PD patients were sleep disorders, Alzheimer's disease, depression, lower urinary tract symptoms and constipation, with the proportions of 6.79, 3.87, 3.72, 3.32 and 2.40%, respectively. Meanwhile, the proportions of sleep disorders, Alzheimer's disease, and constipation were gradually increasing from 2016 to 2019. The characteristics of PD patients and the incidence of its complications were evaluated in the present prescription survey. These updated data provide evidence for further implementation of PD management.

GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.