TITLE:
Discrimination between Dementia Groups and Healthy Elderlies Using Scalp-Recorded-EEG-Based Brain Functional Connectivity Networks
AUTHORS:
Sakura Nishijima, Tetsushi Yada, Toshimasa Yamazaki, Yoshiyuki Kuroiwa, Makoto Nakane, Kimihiro Fujino, Toshiaki Hirai, Yasuhisa Baba, Shoko Yamada, Sho Tsukiyama
KEYWORDS:
Electroencephalography, Network, Dementia, Synchronization Likelihood, Leave-One-Out Cross Validation, Euclidean Distance
JOURNAL NAME:
Journal of Biomedical Science and Engineering,
Vol.13 No.7,
July
30,
2020
ABSTRACT: Objective: To establish a practical method for discriminating dementia groups and healthy elderlies, by using scalp-recorded electroencephalograms (EEGs). Methods: 16-ch EEGs were recorded during resting state for 39 dementia groups and 11 healthy elderlies. The connectivity between any two electrodes was estimated by synchronization likelihood (SL). The brain networks were constructed by normalized SL values. The present leave-one-out cross validation (LOOCV) required the Euclidean distance between any two subjects having 120-dimensional vectors concerned with the SL values for six frequency bands. In order to investigate factors which would affect the LOOCV results, principal component analysis (PCA) was applied to all the subjects. Results: The accuracy for the upper alpha yielded more than 80% and 70% in the dementia groups and the healthy elderlies, respectively. The LOOCV result could be explained in terms of brain networks such as executive control network (ECN) and default mode network (DMN) characterized by factor loadings of principal components. Conclusions: Dementia groups and healthy elderlies could be characterized by principal components of SL values between all the electrode pairs, even less connections, which revealed disruption and preservation of DMN and ECN. Significance: This study will provide a simple and practical method for discriminating dementia groups from healthy elderlies by scalp-recorded EEGs.