University of California, San Diego, La Jolla, CA, USA.
Carnegie Mellon University, Pittsburgh, PA, USA.
Clin EEG Neurosci. 2023 Jul;54(4):434-445. doi: 10.1177/15500594231165589. Epub 2023 Jun 7.
Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/f) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better-predicted group status than traditional band-limited oscillatory power in classification analysis. Aperiodic activity also outperformed the predictions made using participants' behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls.
精神分裂症的诊断和症状严重程度与神经振荡频带（包括θ、α、β和γ）的不规则性有关。然而，脑电图信号既包括周期性活动，也包括非周期性活动，其功率谱特征为（1/f）形状。在本文中，我们在目标检测任务中研究了精神分裂症患者和健康对照组之间的振荡和非周期性活动差异。将信号分离为周期性和非周期性成分后发现，在分类分析中，功率谱的陡度比传统的带限振荡功率更能准确地预测组间状态。而非周期性活动的预测能力也优于使用参与者行为反应进行的预测。此外，在所有电极上，非周期性活动的差异都高度一致。总的来说，与振荡相比，非周期性活动似乎是一种更准确、更稳健的方法，可以将精神分裂症患者与健康对照组区分开来。
