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Implication of Electrophysiological Biomarkers in Psychosis: Focusing on Diagnosis and Treatment Response

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Abstract
Precision medicine has been considered a promising approach to diagnosis, treatment, and various interventions, considering the individual clinical and biological characteristics. Recent advances in biomarker development hold promise for guiding a new era of precision medicine style trials for psychiatric illnesses, including psychosis. Electroencephalography (EEG) can directly measure the full spatiotemporal dynamics of neural activation associated with a wide variety of cognitive processes. This manuscript reviews three aspects: prediction of diagnosis, prognostic aspects of disease progression and outcome, and prediction of treatment response that might be helpful in understanding the current status of electrophysiological biomarkers in precision medicine for patients with psychosis. Although previous EEG analysis could not be a powerful method for the diagnosis of psychiatric illness, recent methodological advances have shown the possibility of classifying and detecting mental illness. Some event-related potentials, such as mismatch negativity, have been associated with neurocognition, functioning, and illness progression in schizophrenia. Resting state studies, sophisticated ERP measures, and machine-learning approaches could make technical progress and provide important knowledge regarding neurophysiology, disease progression, and treatment response in patients with schizophrenia. Identifying potential biomarkers for the diagnosis and treatment response in schizophrenia is the first step towards precision medicine.
All Author(s)
H. S. Lee ; J. S. Kim
Intsitutional Author(s)
이호성김지선
Issued Date
2022
Type
Article
Keyword
biomarkerelectroencephalographyevent-related potentialprecision medicineprediction modeling
Publisher
MDPI
ISSN
2075-4426
Citation Title
Journal of personalized medicine
Citation Volume
12
Citation Number
1
Citation Start Page
31
Citation End Page
31
Language(ISO)
eng
DOI
10.3390/jpm12010031
URI
http://schca-ir.schmc.ac.kr/handle/2022.oak/3589
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