168
S.-H. Lee and Y. Park
scores [107]. Lee and colleagues inspected the global synchronization index (GSI),
which quantifies synchronization between neuronal signals at multiple sites [71].
They found that in patients with AD, GSI values were negatively correlated with
MMSE scores in the delta bands, but positively correlated in the beta1 and gamma
band. In addition, GSI values were positively correlated with CDR scores in the
delta bands, but negatively correlated in the gamma band. The EEG measurements
could be promising biomarker of dementia. However, tight artifact removal and data
collection criteria should precede the measurements for standardized settings for
researchers working in various experimental situations.
7.5 Conclusion
In this chapter, we reviewed possible EEG biomarkers of major psychiatric disorders. Among the various EEG characteristics, P300, functional connectivity network,
LDAEP, ASSR, MMN, and alpha asymmetry are promising EEG biomarkers that
demand more attention in the future. In concordance with the development of computational analytic methods, source level functional connectivity network could make
a new breakthrough for EEG based diagnosis of various neuropsychiatric disorder,
namely in schizophrenia, PTSD, major depressive disorder, and dementia of various
types. In patients with major depressive disorder, alpha asymmetry could be a promising biomarker. However, more research should be done to uncover the confounding
factor of the core pathophysiology of major depressive disorders.
Sensor-level analysis could be contaminated with volume conduction and various
movement artifacts. However, source-level analysis could be used relatively free of
those artifacts, even though there are some technical burden compared to sensor-level
analysis. Importantly, information about brain regional abnormality could be gained
from these source-level analyses, and this information could be a big step to discover
diagnostic and prognosis biomarkers of various neuropsychiatric disorders.
Acknowledgments This research was supported by the Brain Research Program through the
National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future
Planning (NRF-2015M3C7A1028252), the Korea Science and Engineering Foundation (KOSEF),
funded by the Korean government (NRF-2018R1A2A2A05018505), and by the 2017 creative
research program of Inje University.
References
1. H. Aghajani, E. Zahedi, M. Jalili, A. Keikhosravi, B.V. Vahdat, Diagnosis of early Alzheimer’s
disease based on EEG source localization and a standardized realistic head model. IEEE J.
Biomed. Health Inform. 17(6), 1039–45 (2013)
2. J.W. Ashford, K.L. Coburn, T.L. Rose, P.J. Bayley, P300 energy loss in aging and Alzheimer’s
disease. J. Alzheimers Dis. 26(s3), 229–38 (2011)
S.-H. Lee and Y. Park
scores [107]. Lee and colleagues inspected the global synchronization index (GSI),
which quantifies synchronization between neuronal signals at multiple sites [71].
They found that in patients with AD, GSI values were negatively correlated with
MMSE scores in the delta bands, but positively correlated in the beta1 and gamma
band. In addition, GSI values were positively correlated with CDR scores in the
delta bands, but negatively correlated in the gamma band. The EEG measurements
could be promising biomarker of dementia. However, tight artifact removal and data
collection criteria should precede the measurements for standardized settings for
researchers working in various experimental situations.
7.5 Conclusion
In this chapter, we reviewed possible EEG biomarkers of major psychiatric disorders. Among the various EEG characteristics, P300, functional connectivity network,
LDAEP, ASSR, MMN, and alpha asymmetry are promising EEG biomarkers that
demand more attention in the future. In concordance with the development of computational analytic methods, source level functional connectivity network could make
a new breakthrough for EEG based diagnosis of various neuropsychiatric disorder,
namely in schizophrenia, PTSD, major depressive disorder, and dementia of various
types. In patients with major depressive disorder, alpha asymmetry could be a promising biomarker. However, more research should be done to uncover the confounding
factor of the core pathophysiology of major depressive disorders.
Sensor-level analysis could be contaminated with volume conduction and various
movement artifacts. However, source-level analysis could be used relatively free of
those artifacts, even though there are some technical burden compared to sensor-level
analysis. Importantly, information about brain regional abnormality could be gained
from these source-level analyses, and this information could be a big step to discover
diagnostic and prognosis biomarkers of various neuropsychiatric disorders.
Acknowledgments This research was supported by the Brain Research Program through the
National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future
Planning (NRF-2015M3C7A1028252), the Korea Science and Engineering Foundation (KOSEF),
funded by the Korean government (NRF-2018R1A2A2A05018505), and by the 2017 creative
research program of Inje University.
References
1. H. Aghajani, E. Zahedi, M. Jalili, A. Keikhosravi, B.V. Vahdat, Diagnosis of early Alzheimer’s
disease based on EEG source localization and a standardized realistic head model. IEEE J.
Biomed. Health Inform. 17(6), 1039–45 (2013)
2. J.W. Ashford, K.L. Coburn, T.L. Rose, P.J. Bayley, P300 energy loss in aging and Alzheimer’s
disease. J. Alzheimers Dis. 26(s3), 229–38 (2011)