Distinct EEG functional network alterations in Parkinson's disease and multiple system atrophy.

Parkinson's disease (PD) and multiple system atrophy (MSA) are alpha-synuclein-related neurodegenerative disorders with overlapping motor and non-motor symptoms but distinct pathology, clinical management, and prognosis. Early differential diagnosis, particularly between PD and the parkinsonian subtype of MSA (MSA-P), remains a major clinical challenge. Yet, how their distinct underlying neurodegeneration differentially disrupts large-scale neural coordination remains poorly understood, limiting the development of objective neuroimaging biomarkers. Here, we aimed to characterize EEG cortical network alterations in PD and MSA subtypes and explore the potential discriminative value of these features. Using a data-driven masking empirical mode decomposition approach, we isolated cortical oscillatory modes from 97 participants (28 PD, 42 MSA, 27 healthy controls) and computed amplitude- and phase-based functional connectivity. Exploratory network-based statistics identified divergent patterns of disruption, where MSA was characterized by β-band amplitude-based hypoconnectivity, while PD exhibited phase-based decoupling across the δ and β bands. While both disorders shared common network correlates for specific clinical impairments, including α-band phase decoupling for bradykinesia and slow-wave hyperconnectivity for non-motor decline, they also exhibited divergent network associations with symptom severity, highlighting that functional network alterations scale differently with clinical impairment in the two disorders. Finally, employing a target residual machine-learning framework, we conducted an exploratory internal-validation analysis indicating that integrating these EEG network features with standard clinical assessments provided a modest incremental gain in discrimination between PD and MSA-P. These findings highlight the value of source-level EEG functional connectivity in capturing disease-specific neural circuit disruptions, offering candidate features for future validation as objective biomarkers for the differential diagnosis and pathophysiological tracking of parkinsonian disorders.