Dynamic Functional Connectivity (DFC) analysis is a promising approach for the characterization of brain electrophysiological activity. In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings. Brain activity in several well-known frequency bands was first reconstructed using beamforming of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase-locking values, which were obtained from 30 mTBI patients and 50 healthy controls (HC). Subsequently, we estimated normalized Laplacian transformations of individual, statistically and topologically filtered quasi-static graphs. The corresponding eigenvalues of each node synchronization were then computed and through the neural-gas algorithm, we quantized the evolution of the eigenvalues resulting in distinct network microstates (NMstates). The discrimination level between the two groups was assessed using an iterative cross-validation classification scheme with features either the NMstates in each frequency band, or the combination of the so-called chronnectomics (flexibility index, occupancy time of NMstate, and Dwell time) with the complexity index over the evolution of the NMstates across all frequency bands. Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity. However, performance was much higher (accuracy: 91–97%, sensitivity: 100%, and specificity: 77–93%) when focusing on the microstates. Exploring the mean node degree within and between brain anatomical networks (default mode network, frontoparietal, occipital, cingulo-opercular, and sensorimotor), a reduced pattern occurred from lower to higher frequency bands, with statistically significant stronger degrees for the HC than the mTBI group. A higher entropic profile on the temporal evolution of the modularity index was observed for both NMstates for the mTBI group across frequencies. A significant difference in the flexibility index was observed between the two groups for the β frequency band. The latter finding may support a central role of the thalamus impairment in mTBI. The current study considers a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could serve as markers to assess the return of an injured subject back to normality.

中文翻译：

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轻度创伤性脑损伤中异常的全脑转变和自发网络微观状态的动态


动态功能连接（DFC）分析是表征大脑电生理活动的一种有前景的方法。在这项研究中，我们使用源重建脑磁图 (MEG) 静息态记录的 DFC 研究了由于轻度创伤性脑损伤 (mTBI) 引起的异常变化。首先使用 MEG 数据的波束成形重建几个众所周知频段的大脑活动，以确定 90 个感兴趣的大脑解剖区域。使用从 30 名 mTBI 患者和 50 名健康对照 (HC) 获得的锁相值的虚部绘制 DFC 图。随后，我们估计了个体、统计和拓扑过滤的准静态图的归一化拉普拉斯变换。然后计算每个节点同步的相应特征值，并通过神经气体算法，我们量化特征值的演化，从而产生不同的网络微状态（NMstates）。两组之间的区分水平使用迭代交叉验证分类方案进行评估，该方案具有每个频段中的 NMstate 特征，或所谓的时间组学（灵活性指数、NMstate 占用时间和停留时间）与所有频段的 NMstate 演化的复杂性指数。基于时间组学的分类性能显示出 80% 的准确性、99% 的敏感性和 49% 的特异性。然而，当关注微观状态时，性能要高得多（准​​确度：91-97%，灵敏度：100%，特异性：77-93%）。 探索大脑解剖网络（默认模式网络、额顶叶、枕叶、扣带盖骨和感觉运动）内部和之间的平均节点程度，从较低频段到较高频段出现减少模式，统计上 HC 的程度明显强于 mTBI团体。对于跨频率的 mTBI 组的两个 NM 状态，观察到模块化指数的时间演化的较高熵剖面。对于β频段，两组之间的灵活性指数存在显着差异。后一个发现可能支持丘脑损伤在 mTBI 中的核心作用。目前的研究考虑了 mTBI 引起的大脑连接改变的一整套频率依赖性连接组标记物，这些标记物可能可以作为评估受伤受试者恢复正常的标记物。