QEEG‐based differentiation between Alzheimer’s disease with or without alpha synucleinopathy

Abstract

AbstractBackgroundAs existence of alpha synucleniopathy could affect progression of cognitive impairment in Alzheimer’s disease (AD), it’s very important to differentiate comorbidity of alpha synucleniopathy in AD to predict progression. To develop affordable EEG‐based discriminating machine‐learning(ML) algorithm for existence of alpha synucleniopathy, we explore differences between AD with vs without it.MethodBased on pattern of cognitive impairment and 3 types of PET scan [18F‐Florbetaben brain amyloid‐beta, FDG, DAT‐PET] , dementia due to Alzheimer’s disease [pure ADD] or Lewy Body (pure LBD) or mixed type (AD with LB disease) were clinically classified.We measured 19ch resting state EEG based on international 10‐20. Quantitative analysis of EEG was done by iSyncBrain®.ResultPure ADD showed the characteristics that general EEG slowing with low total power, relative delta enhancement and desynchronized alpha with low amplitude. Pure LBD showed the pattern of slow alpha peak frequency with intact synchronization, relatively higher EEG total power and frontotemporal theta enhancement. Compared to pure ADD, ADD with LB showed mixed pattern of ADD and LDB. Total power was higher, alpha wave showed intact synchronization but theta power was rather enhanced at frontal(Fz; p‐value < 0.05) and bilateral temporal(T3,5, T4,6; p‐value < 0.05) area that was reported as characteristic pattern of progressive type of AD by previous studies.ConclusionThese findings imply that ADD could have different EEG oscillation characteristics according to the Lewybody disease. Next step, we will develop the machine‐learning algorithm to discriminate the Lewybody disease based on specific EEG features and validate it.