Antonakakis Marios, Dimitriadis Stavros I, Zervakis Michalis, Papanicolaou Andrew C, Zouridakis George
Institute of Biomagnetism and Biosignal Analysis, Westfalian Wilhelms-University MuensterMuenster, Germany.
Digital Image and Signal Processing Laboratory, School of Electronic and Computer Engineering, Technical University of CreteChania, Greece.
Front Hum Neurosci. 2017 Aug 30;11:416. doi: 10.3389/fnhum.2017.00416. eCollection 2017.
Functional brain connectivity networks exhibit "small-world" characteristics and some of these networks follow a "rich-club" organization, whereby a few nodes of high connectivity (hubs) tend to connect more densely among themselves than to nodes of lower connectivity. The Current study followed an "attack strategy" to compare the rich-club and small-world network organization models using Magnetoencephalographic (MEG) recordings from mild traumatic brain injury (mTBI) patients and neurologically healthy controls to identify the topology that describes the underlying intrinsic brain network organization. We hypothesized that the reduction in global efficiency caused by an attack targeting a model's hubs would reveal the "true" underlying topological organization. Connectivity networks were estimated using mutual information as the basis for cross-frequency coupling. Our results revealed a prominent rich-club network organization for both groups. In particular, mTBI patients demonstrated hyper-synchronization among rich-club hubs compared to controls in the δ band and the δ-γ, θ-γ, and β-γ frequency pairs. Moreover, rich-club hubs in mTBI patients were overrepresented in right frontal brain areas, from θ to γ frequencies, and underrepresented in left occipital regions in the δ-β, δ-γ, θ-β, and β-γ frequency pairs. These findings indicate that the rich-club organization of resting-state MEG, considering its role in information integration and its vulnerability to various disorders like mTBI, may have a significant predictive value in the development of reliable biomarkers to help the validation of the recovery from mTBI. Furthermore, the proposed approach might be used as a validation tool to assess patient recovery.
功能性脑连接网络呈现出“小世界”特征，其中一些网络遵循“富俱乐部”组织形式，即少数高连接性节点（枢纽）之间的连接往往比它们与低连接性节点之间的连接更为密集。本研究采用“攻击策略”，通过对轻度创伤性脑损伤（mTBI）患者和神经健康对照者的脑磁图（MEG）记录进行分析，比较富俱乐部和小世界网络组织模型，以确定描述潜在内在脑网络组织的拓扑结构。我们假设，针对模型枢纽的攻击所导致的全局效率降低将揭示“真正的”潜在拓扑组织。以互信息作为交叉频率耦合的基础来估计连接网络。我们的结果显示，两组均存在显著的富俱乐部网络组织。特别是，与对照组相比，mTBI患者在δ频段以及δ-γ、θ-γ和β-γ频率对中，富俱乐部枢纽之间表现出超同步。此外，mTBI患者的富俱乐部枢纽在右额叶脑区从θ到γ频率上过度代表，而在左枕叶区域，在δ-β、δ-γ、θ-β和β-γ频率对中则代表不足。这些发现表明，静息态MEG的富俱乐部组织，考虑到其在信息整合中的作用以及对诸如mTBI等各种疾病的易损性，可能在开发可靠的生物标志物以帮助验证mTBI恢复方面具有重要的预测价值。此外，所提出的方法可能用作评估患者恢复的验证工具。
