Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.
Institute for Systems and Robotics - Lisboa, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa,1049-001 Lisbon, Portugal.
Cereb Cortex. 2023 Mar 21;33(7):3816-3826. doi: 10.1093/cercor/bhac309.
Research on schizophrenia typically focuses on one paradigm for which clear-cut differences between patients and controls are established. Great efforts are made to understand the underlying genetical, neurophysiological, and cognitive mechanisms, which eventually may explain the clinical outcome. One tacit assumption of these "deep rooting" approaches is that paradigms tap into common and representative aspects of the disorder. Here, we analyzed the resting-state electroencephalogram (EEG) of 121 schizophrenia patients and 75 controls. Using multiple signal processing methods, we extracted 194 EEG features. Sixty-nine out of the 194 EEG features showed a significant difference between patients and controls, indicating that these features detect an important aspect of schizophrenia. Surprisingly, the correlations between these features were very low. We discuss several explanations to our results and propose that complementing "deep" with "shallow" rooting approaches might help in understanding the underlying mechanisms of the disorder.
精神分裂症的研究通常集中在一个范式上，该范式为患者和对照组之间建立了明确的差异。人们做出了巨大的努力来了解潜在的遗传、神经生理和认知机制，这些机制最终可能解释临床结果。这些“深入根源”方法的一个隐含假设是，这些范式涉及到疾病的常见和代表性方面。在这里，我们分析了 121 名精神分裂症患者和 75 名对照者的静息态脑电图 (EEG)。使用多种信号处理方法，我们提取了 194 个 EEG 特征。在 194 个 EEG 特征中有 69 个在患者和对照组之间表现出显著差异，表明这些特征检测到精神分裂症的一个重要方面。令人惊讶的是，这些特征之间的相关性非常低。我们讨论了对我们结果的几种解释，并提出用“浅”根源方法补充“深”根源方法可能有助于理解该疾病的潜在机制。
