ArticleNPJ Parkinson's disease2025
Characterizing the heterogeneity of neurodegenerative diseases through EEG normative modeling.
Article in NPJ Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dynamic Mode Decomposition-Based Clustered Pattern Projection for Reliable Alzheimer's Disease Detection from EEG.Diagnostics (Basel, Switzerland) · 2026Article
- Normative Modeling of EEG Complexity Reveals Cortical Rigidity Associated with Pain Severity in Chronic Pain.Journal of pain research · 2026Article
- The use of electroencephalography in neurodegenerative disease and its utility in dementia.NPJ dementia · 2026Review
- Beyond homogeneity: charting the landscape of heterogeneity in neurodevelopmental and psychiatric electroencephalography.Translational psychiatry · 2025Article
- From early to contemporary normative modeling: Mapping individual differences in neurophysiological signals.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
13 authors.
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Abstract
Neurodegenerative diseases like Parkinson's (PD) and Alzheimer's (AD) exhibit considerable heterogeneity of functional brain features within patients, complicating diagnosis and treatment. Here, we use electroencephalography (EEG) and normative modeling to investigate neurophysiological mechanisms underpinning this heterogeneity. Resting-state EEG data from 14 clinical units included healthy adults (n = 499) and patients with PD (n = 237) and AD (n = 197), aged over 40. Spectral and source connectivity analyses provided features for normative modeling, revealing significant, frequency-dependent EEG deviations with high heterogeneity in PD and AD. Around 30% of patients exhibited spectral deviations, while ~80% showed functional source connectivity deviations. Notably, the spatial overlap of deviant features did not exceed 60% for spectral and 25% for connectivity analysis. Furthermore, patient-specific deviations correlated with clinical measures, with greater deviations linked to worse UPDRS for PD (⍴ = 0.24, p = 0.025) and MMSE for AD (⍴ = -0.26, p = 0.01). These results suggest that EEG deviations could enrich individualized clinical assessment in Precision Neurology.
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