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<p>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 (<i>n</i> = 499) and patients with PD (<i>n</i> = 237) and AD (<i>n</i> = 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 (<i>⍴</i> = 0.24, <i>p</i> = 0.025) and MMSE for AD (<i>⍴</i> = −0.26, <i>p</i> = 0.01). These results suggest that EEG deviations could enrich individualized clinical assessment in Precision Neurology.</p>

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Characterizing the heterogeneity of neurodegenerative diseases through EEG normative modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月8日

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  • Judie Tabbal,
  • Aida Ebadi,
  • Ahmad Mheich,
  • Aya Kabbara,
  • Bahar Güntekin,
  • Görsev Yener,
  • Veronique Paban,
  • Ute Gschwandtner,
  • Peter Fuhr,
  • Marc Verin,
  • Claudio Babiloni,
  • Sahar Allouch,
  • Mahmoud Hassan

摘要

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.