借助电生理工具了解精神疾病的病理生理学和早期诊断:一些见解和经验事实。

Tomiki Sumiyoshi, Salvatore Campanella, Giulia Maria Giordano, Ryouhei Ishii, Oliver Pogarell
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引用次数: 0

摘要

目的。神经生理学工具仍然是评估精神疾病不可或缺的工具。这些技术来源广泛、价格低廉、耐受性好,为评估大脑功能改变提供了途径。在临床精神病治疗中,电生理技术需要提供有关大脑功能的重要信息。虽然这些技术在日常临床应用(紧急评估、排除大脑器质性病变)中有直接的好处,但这些工具在监测精神疾病的进展或治疗效果方面也很有用。越来越多的证据和令人信服的文献证实,脑电图和相关技术有助于诊断工作、确定疾病类别的亚组、评估长期病因和促进反应预测。方法和结果。在本报告中,我们重点介绍了神经生理学技术在 3 种高发精神疾病中应用的 3 种不同的新进展:(1) 脑电图记录和机器学习分析(深度学习)在改善痴呆亚型诊断中的价值;(2) 错配负性在精神分裂症早期诊断中的应用;(3) 利用认知事件相关电位监测成瘾和预防复发。介绍了经验证据。结论。这些信息强调了神经生理学工具在确定有用的生物标记方面的重要作用,从而提高护理管理的效率。会议还讨论了实施机器学习方法以及开展大型横断面和纵向研究的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding the Pathophysiology of Mental Diseases and Early Diagnosis Thanks to Electrophysiological Tools: Some Insights and Empirical Facts.

Objective. Neurophysiological tools remain indispensable instruments in the assessment of psychiatric disorders. These techniques are widely available, inexpensive and well tolerated, providing access to the assessment of brain functional alterations. In the clinical psychiatric context, electrophysiological techniques are required to provide important information on brain function. While there is an immediate benefit in the clinical application of these techniques in the daily routine (emergency assessments, exclusion of organic brain alterations), these tools are also useful in monitoring the progress of psychiatric disorders or the effects of therapy. There is increasing evidence and convincing literature to confirm that electroencephalography and related techniques can contribute to the diagnostic workup, to the identification of subgroups of disease categories, to the assessment of long-term causes and to facilitate response predictions. Methods and Results. In this report we focus on 3 different novel developments of the use of neurophysiological techniques in 3 highly prevalent psychiatric disorders: (1) the value of EEG recordings and machine learning analyses (deep learning) in order to improve the diagnosis of dementia subtypes; (2) the use of mismatch negativity in the early diagnosis of schizophrenia; and (3) the monitoring of addiction and the prevention of relapse using cognitive event-related potentials. Empirical evidence was presented. Conclusion. Such information emphasized the important role of neurophysiological tools in the identification of useful biological markers leading to a more efficient care management. The potential of the implementation of machine learning approaches together with the conduction of large cross-sectional and longitudinal studies was also discussed.

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