Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury.
Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury.
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DOI：
10.1016/j.nicl.2015.09.011
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Papanicolaou AC
中科院分区：
文献类型：
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作者：
Dimitriadis SI;Zouridakis G;Rezaie R;Babajani-Feremi A;Papanicolaou AC
轻度创伤性脑损伤(MTBI)可能通过破坏调节脑区之间有效沟通的功能连接网络来影响正常的认知和行为。在这项研究中，我们分析了来自31名mTBI患者和55名正常对照的静息状态脑磁图(MEG)记录的脑连接特征。我们使用锁相值估计来计算函数连通图，以量化不同频段的传感器之间的特定频率耦合。总体而言，正常对照组显示密集的强本地连接网络和有限数量的远程连接，约占所有连接的20%，而mTBI患者显示的网络特征是弱本地连接和强远程连接，占所有连接的60%以上。用张量表示连通图和用张量子空间分析进行最佳特征提取，比较了不同频率下两种不同的一般模式，结果表明，在α波段，mTBI患者可以100%的分类准确率与正常对照组区分开来。这些令人鼓舞的发现支持这样的假设，即基于脑磁图的功能连接模式可能被用作生物标志物，可以提供更准确的诊断，帮助指导治疗，并监测mTBI干预的有效性。我们分析了31名mTBI患者和55名对照的静息状态连接性特征。我们使用锁相值来量化特定频率的连接耦合。正常控制网络表现为密集的局部连接和稀疏的远程连接。颅脑损伤患者网络表现为局部连接稀疏，远程连接密集。张量子空间分析可以在α波段上100%准确地对对象进行分类
Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI. We analyzed resting state connectivity profiles in 31 mTBI patients and 55 controls. We quantified frequency-specific connectivity couplings using phase-locking values. Normal control networks showed dense local and sparse long-range connections. TBI patient networks showed sparse local and dense long-range connections. Tensor subspace analysis could classify subjects with 100% accuracy in the α band