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精神分裂症词汇加工相关异常神经振荡的多维分析。

Multidimensional analysis of the abnormal neural oscillations associated with lexical processing in schizophrenia.

机构信息

Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, MN, USA.

出版信息

Clin EEG Neurosci. 2013 Apr;44(2):135-43. doi: 10.1177/1550059412465078. Epub 2013 Mar 19.

Abstract

The neural mechanisms of language abnormalities, the core symptoms in schizophrenia, remain unclear. In this study, a new experimental paradigm, combining magnetoencephalography (MEG) techniques and machine intelligence methodologies, was designed to gain knowledge about the frequency, brain location, and time of occurrence of the neural oscillations that are associated with lexical processing in schizophrenia. The 248-channel MEG recordings were obtained from 12 patients with schizophrenia and 10 healthy controls, during a lexical processing task, where the patients discriminated correct from incorrect lexical stimuli that were visually presented. Event-related desynchronization/synchronization (ERD/ERS) was computed along the frequency, time, and space dimensions combined, that resulted in a large spectral-spatial-temporal ERD/ERS feature set. Machine intelligence techniques were then applied to select a small subset of oscillation patterns that are abnormal in patients with schizophrenia, according to their discriminating power in patient and control classification. Patients with schizophrenia showed abnormal ERD/ERS patterns during both lexical encoding and post-encoding periods. The top-ranked features were located at the occipital and left frontal-temporal areas, and covered a wide frequency range, including δ (1-4 Hz), α (8-12 Hz), β (12-32 Hz), and γ (32-48 Hz) bands. These top features could discriminate the patient group from the control group with 90.91% high accuracy, which demonstrates significant brain oscillation abnormalities in patients with schizophrenia at the specific frequency, time, and brain location indicated by these top features. As neural oscillation abnormality may be due to the mechanisms of the disease, the spectral, spatial, and temporal content of the discriminating features can offer useful information for helping understand the physiological basis of the language disorder in schizophrenia, as well as the pathology of the disease itself.

摘要

语言异常的神经机制是精神分裂症的核心症状,但目前仍不清楚。在这项研究中,设计了一种新的实验范式,结合脑磁图(MEG)技术和机器智能方法,旨在了解与精神分裂症词汇处理相关的神经振荡的频率、大脑位置和出现时间。在词汇处理任务中,从 12 名精神分裂症患者和 10 名健康对照者获得了 248 通道 MEG 记录,在此任务中,患者通过视觉呈现来区分正确和错误的词汇刺激。在沿频率、时间和空间维度组合计算事件相关去同步/同步(ERD/ERS)后,得到了一个大型的光谱-空间-时间 ERD/ERS 特征集。然后,应用机器智能技术根据其在患者和对照分类中的区分能力,从该特征集中选择一小部分在精神分裂症患者中异常的振荡模式。精神分裂症患者在词汇编码和后编码期间均表现出异常的 ERD/ERS 模式。排名靠前的特征位于枕部和左侧额颞区,覆盖了广泛的频率范围,包括 δ(1-4 Hz)、α(8-12 Hz)、β(12-32 Hz)和 γ(32-48 Hz)频段。这些顶级特征可以以 90.91%的高准确率区分患者组和对照组,这表明精神分裂症患者在特定频率、时间和大脑位置存在明显的脑振荡异常,这些特征表明了这些异常。由于神经振荡异常可能是由于疾病的机制,因此区分特征的光谱、空间和时间内容可以为帮助理解精神分裂症语言障碍的生理基础以及疾病本身的病理学提供有用的信息。

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