Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury
Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury
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DOI：
10.1016/j.ijpsycho.2016.02.002
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发表时间：
2016-04-01
影响因子：
3
通讯作者：
Papanicolaou, Andrew C.
中科院分区：
文献类型：
--
作者：
Antonakakis, Marios;Dimitriadis, Stavros I.;Papanicolaou, Andrew C.
交叉频率耦合（CFC）被认为是跨远端脑区神经网络功能整合的一种基本机制。在这项研究中，我们分析了30名轻度创伤性脑损伤（mTBI）患者和50名对照者静息状态脑磁图（MEG）记录的CFC谱。我们使用互信息（MI）来量化六个非重叠频段记录传感器之间的相幅耦合（PAC）活动。在形成基于cfc的功能连通性图后，我们采用张量表示和张量子空间分析来确定主题分类为mTBI或控制的最优特征集。我们的研究结果表明，与mTBI患者相比，对照组形成了一个更强的局部和全局连接的密集网络，表明功能整合程度更高。此外，mTBI患者可以与对照组区分，分类准确率超过90%。这些发现表明，静息状态脑磁图计算的脑网络分析和连接谱的张量表示可能为mTBI的诊断提供有价值的生物标志物。(C) 2016 Elsevier B.V.版权所有
Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI. (C) 2016 Elsevier B.V. All rights reserved.