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Oscillatory characteristics of resting-state magnetoencephalography reflect pathological and symptomatic conditions of cognitive impairment.

Hideyuki HoshiYoko HirataKeisuke FukasawaMomoko KobayashiYoshihito Shigihara
Published in: Frontiers in aging neuroscience (2024)
MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care.
Keyphrases
  • resting state
  • functional connectivity
  • high frequency
  • cognitive impairment
  • healthcare
  • machine learning
  • big data
  • deep learning
  • social media