AI Article Synopsis

  • This study investigates the effectiveness of magnetoencephalography (MEG) in detecting early-stage Alzheimer's disease by comparing MEG data from patients with mild cognitive impairment and AD dementia against cognitively normal individuals.
  • The researchers analyzed various MEG power measurements across different frequency bands during resting states, finding notable differences in whole cerebral normalization (WCN) power between the groups.
  • The study achieved a high discrimination accuracy of 83% using a Support Vector Machine classifier, indicating that MEG is a promising tool for distinguishing early-stage Alzheimer's from normal cognitive function.

Article Abstract

This study aimed to examine whether magnetoencephalography (MEG) is useful to detect early stage Alzheimer's disease (AD). We analyzed MEG data from the early stage AD group (n = 20; 6 with mild cognitive impairment due to AD and 14 with AD dementia) and cognitively normal control group (NC, n = 27). MEG was recorded during resting eyes closed (EC) and eyes open (EO), and the following 6 values for each of 5 bands (θ1: 4-6, θ2: 6-8, α1: 8-10, α2: 10-13, β: 13-20 Hz) in the cerebral 68 regions were compared between the groups: (1) absolute power during EC and (2) EO, (3) whole cerebral normalization (WCN) power during EC and (4) EO, (5) difference of the absolute powers between the EC and EO conditions (the EC-EO difference), and (6) WCN value of the EC-EO difference. We found significant differences between the groups in the WCN powers during the EO condition, and the EC-EO differences. Using a Support Vector Machine classifier, a discrimination accuracy of 83% was obtained and an AUC in an ROC analysis was 0.91. This study demonstrates that MEG during resting EC and EO is useful in discriminating between early stage AD and NC.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7272642PMC
http://dx.doi.org/10.1038/s41598-020-66034-5DOI Listing

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