非周期性神经活动比神经振荡更能预测精神分裂症。

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY Clinical EEG and Neuroscience Pub Date : 2023-07-01 DOI:10.1177/15500594231165589
Erik J Peterson, Burke Q Rosen, Aysenil Belger, Bradley Voytek, Alana M Campbell
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引用次数: 20

摘要

精神分裂症的诊断和症状严重程度与神经振荡频带的不规则性有关,包括θ、α、β和γ。然而,脑电图信号包括周期性和非周期性活动,其特征是功率谱中的(1/fX)形状。在本文中,我们研究了精神分裂症患者和健康对照者在目标检测任务中振荡和非周期活动的差异。周期和非周期分量的分离表明,在分类分析中,功率谱的陡峭度比传统的带限振荡功率更能预测群体状态。非周期性活动的表现也优于根据参与者的行为反应做出的预测。此外,非周期活性的差异在所有电极上都是高度一致的。总之,与振荡相比,非周期活动似乎是区分精神分裂症患者与健康对照的更准确和更可靠的方法。
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Aperiodic Neural Activity is a Better Predictor of Schizophrenia than Neural Oscillations.

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/fX) 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.

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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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