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The application of a mathematical model linking structural and functional connectomes in severe brain injury

机译:连接结构和功能连接体的数学模型在严重脑损伤中的应用

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Following severe injuries that result in disorders of consciousness, recovery can occur over many months or years post-injury. While post-injury synaptogenesis, axonal sprouting and functional reorganization are known to occur, the network-level processes underlying recovery are poorly understood. Here, we test a network-level functional rerouting hypothesis in recovery of patients with disorders of consciousness following severe brain injury. This hypothesis states that the brain recovers from injury by restoring normal functional connections via alternate structural pathways that circumvent impaired white matter connections. The so-called network diffusion model, which relates an individual's structural and functional connectomes by assuming that functional activation diffuses along structural pathways, is used here to capture this functional rerouting. We jointly examined functional and structural connectomes extracted from MRIs of 12 healthy and 16 brain-injured subjects. Connectome properties were quantified via graph theoretic measures and network diffusion model parameters. While a few graph metrics showed groupwise differences, they did not correlate with patients' level of consciousness as measured by the Coma Recovery Scale — Revised. There was, however, a strong and significant partial Pearson's correlation (accounting for age and years post-injury) between level of consciousness and network diffusion model propagation time ( r = 0.76, p 0.05, corrected), i.e. the time functional activation spends traversing the structural network. We concluded that functional rerouting via alternate (and less efficient) pathways leads to increases in network diffusion model propagation time. Simulations of injury and recovery in healthy connectomes confirmed these results. This work establishes the feasibility for using the network diffusion model to capture network-level mechanisms in recovery of consciousness after severe brain injury. Highlights ? A “functional rerouting” hypothesis in recovery from brain injury is tested. ? The connectome-based network diffusion model measures functional rerouting. ? Recovery in severe brain injury correlates with a network diffusion model parameter. ? Simulation in healthy connectomes independently validates the results in patients.
机译:在导致意识障碍的严重伤害之后,恢复可能会在受伤后数月或数年内发生。虽然已知会发生损伤后突触发生,轴突萌发和功能重组,但对恢复基础的网络级过程知之甚少。在这里,我们测试了在严重脑损伤后意识障碍患者的康复中的网络级功能重新路由假设。该假设指出,大脑可以通过替代性的结构途径来恢复正常的功能连接,从而避免受伤的白质连接而从损伤中恢复过来。所谓的网络扩散模型通过捕获功能激活沿结构途径扩散来关联个人的结构和功能连接体,在此用于捕获此功能重新路由。我们联合检查了从12名健康受试者和16名脑损伤受试者的MRI中提取的功能和结构连接体。通过图论方法和网络扩散模型参数对连接组特性进行了定量。尽管一些图形指标显示了分组差异,但它们与昏迷恢复量表(修订版)所衡量的患者意识水平无关。但是,意识水平与网络扩散模型的传播时间(r = 0.76,p <0.05,已校正)之间存在强烈且显着的部分皮尔逊相关性(占伤害的年龄和受伤年份),即功能激活花费的时间遍历结构网络。我们得出的结论是,通过备用(且效率较低)路径进行的功能重新路由会导致网络扩散模型的传播时间增加。健康的连接体损伤和恢复的模拟证实了这些结果。这项工作建立了使用网络扩散模型捕获严重脑损伤后意识恢复的网络级机制的可行性。强调 ?测试了从脑损伤中恢复的“功能重新路由”假说。 ?基于连接组的网络扩散模型可测量功能重新路由。 ?严重脑损伤的恢复与网络扩散模型参数相关。 ?在健康的连接体中进行仿真可独立验证患者的结果。

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