Disruption of low-frequency narrowband EEG microstate networks in Parkinson's disease with mild cognitive impairment.

Abstract

Background: Electroencephalogram (EEG) microstates provide insights into large-scale brain network coordination, revealing distinct neural dynamics within specific frequency bands associated with cognitive processes and neurological disorders. Critical gaps remain regarding the abnormalities of narrowband microstate networks in Parkinson's disease with mild cognitive impairment (PD-MCI), a key prodromal stage of the development of PD dementia. Given the importance of early detection and understanding of cognitive decline in PD-MCI, this study investigated whether alterations in narrowband EEG microstate networks could serve as early electrophysiological biomarkers for cognitive decline in PD-MCI.

Method: Forty-seven individuals with PD (21 with MCI and 26 cognitively normal [PD-NC]) and 20 healthy controls were recruited. For both broadband and narrowband EEG microstates, the phase lag index was used to construct microstate brain networks, and their spatiotemporal variability was assessed.

Results: Microstate analysis revealed significant divergence in narrowband parameters exclusively between the PD-MCI and PD-NC cohorts. PD-MCI showed a significant increase in low-frequency (delta/alpha-band) microstate class A, while delta-band microstate class D exhibited a significant reduction. The microstate network patterns of PD-MCI were characterized by diminished stability and disrupted synchronization in delta microstate class A within the frontal region, theta microstate class D within central region, and theta microstate class B within the occipital region. These neurophysiological markers specific to PD-MCI were significantly correlated with Montreal Cognitive Assessment scores, and machine learning-based analyses further validated their diagnostic efficacy, with accuracy ranging from 94 to 98%.

Conclusions: This study identified unique abnormalities in narrowband microstate dynamics within neural networks of individuals with PD-MCI, revealing promising electrophysiological markers for the early detection and longitudinal monitoring of cognitive decline. Furthermore, these findings suggest potential applications in precision rehabilitation, whereby frequency-specific microstate biomarkers could guide individualized interventions and monitor therapeutic efficacy.