EEG Slowing and Axial Motor Impairment Are Independent Predictors of Cognitive Worsening in a Three-Year Cohort of Patients With Parkinson's Disease

Exploring the complexity of EEG patterns in Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20 Hz) and beta 2 (20-30 Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.

Polysomnographic Predictors of Sleep, Motor, and Cognitive Dysfunction Progression in Parkinson's Disease

PURPOSE OF REVIEW

Sleep disturbances are an important nonmotor feature of Parkinson's disease (PD) that can cause polysomnographic (PSG) alterations. These alterations are already present in early PD and may be associated with a specific disease course. This systematic review describes the role of PSG variables as predictors of sleep dysfunction, motor and cognitive dysfunction progression in PD.

RECENT FINDINGS

Nineteen longitudinal cohort studies were included. Their main findings were that (1) REM sleep behavioral events, REM sleep without atonia (RSWA), and electroencephalography (EEG) changes (mainly microsleep instability) are predictors of the development of REM sleep behavior disorder (RBD); (2) RBD, RSWA, and lower slow-wave sleep energy predict motor progression; (3) RBD, EEG slowing, and sleep spindles changes are predictors of cognitive deterioration; and (4) OSA is associated with severe motor and cognitive symptoms at baseline, with inconsistent findings on the effect of continuous positive airway pressure (CPAP) therapy for these symptoms. The results of our systematic review support a role of the video-PSG in disease progression prediction in PD and its usefulness as a biomarker. However, future studies are needed to investigate whether treatment of these PSG abnormalities and sleep disturbances may have a neuroprotective effect on disease progression.