Network disruption based on multi-modal EEG-MRI in α-synucleinopathies.

Area of Science:

• Neuroscience
• Neurology
• Biomarker Discovery

Background:

• Brain network dysfunction is evident in prodromal synucleinopathies using resting-state electroencephalography (EEG) and magnetic resonance imaging (MRI).
• Isolated rapid eye movement sleep behavior disorder (iRBD) represents a prodromal stage for synucleinopathies.

Purpose of the Study:

• To identify multimodal electrophysiological and neuroimaging biomarkers for differential diagnosis in synucleinopathies (multiple system atrophy [MSA] and Parkinson's disease [PD]).
• To detect phenoconversion in isolated rapid eye movement sleep behavior disorder (iRBD).

Main Methods:

• Quantitative EEG analysis of power spectral density (PSD) and functional connectivity (FC) using weighted Phase Lag Index (wPLI).
• MRI FC analysis to confirm network disruptions identified by EEG.
• Development of a multimodal discriminative model integrating EEG and MRI data.

Main Results:

• Significant differences in delta and theta PSD among MSA, PD, and healthy controls (HC).
• Distinct band-specific FC profiles observed, with alpha FC correlating with motor dysfunction and gamma FC distinguishing PD from MSA.
• A multimodal model integrating EEG and MRI achieved an area under the curve of 0.900 for discriminating MSA and PD.
• FC abnormalities were more prominent than spectral features in iRBD, indicating prodromal dysfunction.
• Decreased FC between the angular gyrus and striatum identified in alpha-synucleinopathies, associated with dopaminergic degeneration in iRBD.

Conclusions:

• EEG spectral and functional profiles characterize prodromal and clinical synucleinopathies.
• Multimodal EEG and MRI offer a novel approach for discriminating MSA and PD.
• This approach can monitor neurodegenerative progression in the preclinical phase.