Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia.
Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia.
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DOI：
10.1016/j.nicl.2020.102485
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Jerbi K
中科院分区：
文献类型：
--
作者：
Alamian G;Pascarella A;Lajnef T;Knight L;Walters J;Singh KD;Jerbi K
精神分裂症神经动力学的时间自相关性减弱。主要在α和β频带中观察到变化。机器学习对患者和对照组进行了显著分类，准确率为82%。显示中断的时间连接的大脑区域包括（帕拉）边缘系统区域。长程时间相关性提示精神分裂症患者的时间连接障碍精神分裂症的当前理论强调改变的信息整合作为这种疾病的核心功能障碍的作用。虽然大量的神经影像学证据来自对空间连接的研究，但理解时间中断对于充分捕捉精神分裂症中连接障碍的本质至关重要。最近的电生理学研究表明，长距离时间相关（LRTC）的振幅动力学的神经振荡捕获的完整性，在健康的大脑中传输的信息。因此，在这项研究中，25名精神分裂症患者和25名对照组（8名女性/组）在两个五分钟的静息态脑磁图（一次睁眼，一次闭眼）。我们使用源级分析，通过表征跨皮层和皮层下脑区的LRTC来研究患者的时间连接障碍。除了标准的统计评估外，我们还应用了一种使用支持向量机的机器学习框架来评估LRTC在识别患者和健康对照方面的区分能力。我们发现，精神分裂症患者的神经振荡的特点是减少信号记忆和更高的变异性随时间的推移，证明了皮层和皮层下衰减的LRTC在α和β频段。支持向量机显著分类参与者使用LRTC在关键边缘和边缘脑区，解码准确率达到82%。重要的是，这些大脑区域属于与精神分裂症的神经学高度相关的网络。因此，这些发现将时间连接障碍作为精神分裂症信息处理改变的标志，并有助于推进我们对这种病理学的理解。
Temporal autocorrelation is attenuated in the neural dynamics of schizophrenia. Alterations are primarily observed in the alpha and beta frequency bands. Machine-learning significantly classified patients and controls with 82% accuracy. Brain regions showing disrupted temporal connectivity include (para)limbic areas. Long-range temporal correlation informs on temporal dysconnectivity in schizophrenia. Current theories of schizophrenia emphasize the role of altered information integration as the core dysfunction of this illness. While ample neuroimaging evidence for such accounts comes from investigations of spatial connectivity, understanding temporal disruptions is important to fully capture the essence of dysconnectivity in schizophrenia. Recent electrophysiology studies suggest that long-range temporal correlation (LRTC) in the amplitude dynamics of neural oscillations captures the integrity of transferred information in the healthy brain. Thus, in this study, 25 schizophrenia patients and 25 controls (8 females/group) were recorded during two five-minutes of resting-state magnetoencephalography (once with eyes-open and once with eyes-closed). We used source-level analyses to investigate temporal dysconnectivity in patients by characterizing LRTCs across cortical and sub-cortical brain regions. In addition to standard statistical assessments, we applied a machine learning framework using support vector machine to evaluate the discriminative power of LRTCs in identifying patients from healthy controls. We found that neural oscillations in schizophrenia patients were characterized by reduced signal memory and higher variability across time, as evidenced by cortical and subcortical attenuations of LRTCs in the alpha and beta frequency bands. Support vector machine significantly classified participants using LRTCs in key limbic and paralimbic brain areas, with decoding accuracy reaching 82%. Importantly, these brain regions belong to networks that are highly relevant to the symptomology of schizophrenia. These findings thus posit temporal dysconnectivity as a hallmark of altered information processing in schizophrenia, and help advance our understanding of this pathology.
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