Lee Ho Sung, Kim Ji Sun
Department of Pulmonology and Allergy, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea.
Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea.
J Pers Med. 2022 Jan 2;12(1):31. doi: 10.3390/jpm12010031.
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.
考虑到个体的临床和生物学特征,精准医学被认为是一种用于诊断、治疗及各种干预的有前景的方法。生物标志物开发的最新进展有望为包括精神病在内的精神疾病引领一个精准医学模式试验的新时代。脑电图(EEG)能够直接测量与多种认知过程相关的神经激活的完整时空动态。本文综述了三个方面:诊断预测、疾病进展和预后的预后情况,以及治疗反应预测,这些可能有助于理解精神病患者精准医学中电生理生物标志物的现状。尽管先前的脑电图分析并非诊断精神疾病的有力方法,但最近的方法学进展已显示出对精神疾病进行分类和检测的可能性。一些事件相关电位,如失配负波,已与精神分裂症的神经认知、功能及疾病进展相关联。静息态研究、复杂的ERP测量方法及机器学习方法可能会取得技术进步,并为精神分裂症患者的神经生理学、疾病进展及治疗反应提供重要知识。识别精神分裂症诊断和治疗反应的潜在生物标志物是迈向精准医学的第一步。