Borderline and Depression: A Thin EEG Line.

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY
Jakša Vukojević, Damir Mulc, Ivana Kinder, Eda Jovičić, Krešimir Friganović, Aleksandar Savić, Mario Cifrek, Domagoj Vidović
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Abstract

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.

边缘和抑郁:一条细脑电图线。
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。尽管抑郁症是边缘型人格患者中最常见的合并症之一,但基础研究并没有给我们这两种实体之间的明确区别。抑郁症可以是一种独特的疾病,但也可以是其他精神病理学的症状,这一概念促使我们的团队尝试使用146个脑电图记录和机器学习来描述这两种实体。所使用的算法仅为此目的而开发,无法区分这两种实体,这意味着在给定的数据和使用的方法下,MDD患者的脑电图与诊断为MDD和BPD的患者没有显著差异。通过增加数据集和时空特异性,可以在使用脑电图记录时获得更敏感的诊断方法。据我们所知,这是第一个使用脑电图记录和先进机器学习技术的研究,并进一步证实了这两个实体之间的密切相互关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Clinical EEG and Neuroscience
Clinical EEG and Neuroscience 医学-临床神经学
CiteScore
5.20
自引率
5.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.
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