87137University Psychiatric Hospital Vrapče, University of Zagreb, Zagreb, Croatia.
112586University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia.
Clin EEG Neurosci. 2023 May;54(3):224-227. doi: 10.1177/15500594211060830. Epub 2021 Nov 13.
In everyday clinical practice, there is an ongoing debate about the nature of major depressive disorder (MDD) in patients with borderline personality disorder (BPD). The underlying research does not give us a clear distinction between those 2 entities, although depression is among the most frequent comorbid diagnosis in borderline personality patients. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies led our team to try and delineate those 2 entities using 146 EEG recordings and machine learning. The utilized algorithms, developed solely for this purpose, could not differentiate those 2 entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD respecting the given data and methods used. By increasing the data set and the spatiotemporal specificity, one could have a more sensitive diagnostic approach when using EEG recordings. To our knowledge, this is the first study that used EEG recordings and advanced machine learning techniques and further confirmed the close interrelationship between those 2 entities.
在日常临床实践中,关于边缘型人格障碍(BPD)患者的重度抑郁障碍(MDD)的性质一直存在争议。尽管抑郁是边缘型人格障碍患者最常见的合并诊断之一,但基础研究并未明确区分这两种病症。有一种观点认为,抑郁既可以是一种独立的疾病,也可以是其他精神病理学的症状,这促使我们的团队尝试使用 146 个脑电图记录和机器学习来区分这两种病症。为实现这一目的而专门开发的算法无法区分这两种病症,这意味着患有 MDD 的患者在脑电图方面与患有 MDD 和 BPD 的患者没有显著差异,这是基于给定的数据和使用的方法得出的。通过增加数据集和时空特异性,可以在使用脑电图记录时采用更敏感的诊断方法。据我们所知,这是第一项使用脑电图记录和先进的机器学习技术的研究,并进一步证实了这两种病症之间的密切关系。