Schiller Mark J
Mind Therapy Clinic, San Francisco, CA, United States.
MYnd Analytics, Inc., Mission Viejo, CA, United States.
Front Psychiatry. 2019 Jan 23;9:779. doi: 10.3389/fpsyt.2018.00779. eCollection 2018.
This paper reviews significant contributions to the evidence for the use of quantitative electroencephalography features as biomarkers of depression treatment and examines the potential of such technology to guide pharmacotherapy. Frequency band abnormalities such as alpha and theta band abnormalities have shown promise as have combinatorial measures such as cordance (a measure combining alpha and theta power) and the Antidepressant Treatment Response Index in predicting medication treatment response. Nevertheless, studies have been hampered by methodological problems and inconsistencies, and these approaches have ultimately failed to elicit any significant interest in actual clinical practice. More recent machine learning approaches such as the Psychiatric Encephalography Evaluation Registry (PEER) technology and other efforts analyze large datasets to develop variables that may best predict response rather than test a priori hypotheses. PEER is a technology that may go beyond predicting response to a particular antidepressant and help to guide pharmacotherapy.
本文回顾了将定量脑电图特征用作抑郁症治疗生物标志物的证据所做出的重大贡献，并探讨了此类技术指导药物治疗的潜力。诸如α和θ波段异常等频段异常已显示出前景，像协调性（一种结合α和θ功率的测量方法）和抗抑郁治疗反应指数等组合测量方法在预测药物治疗反应方面也显示出前景。然而，研究受到方法学问题和不一致性的阻碍，这些方法最终未能在实际临床实践中引起任何显著关注。最近的机器学习方法，如精神科脑电图评估注册库（PEER）技术及其他研究，通过分析大型数据集来开发可能最能预测反应的变量，而不是检验先验假设。PEER是一种可能超越预测对特定抗抑郁药反应的技术，并有助于指导药物治疗。
