White Matter Network Architecture Guides Direct Electrical Stimulation through Optimal State Transitions
White Matter Network Architecture Guides Direct Electrical Stimulation through Optimal State Transitions
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DOI：
10.1016/j.celrep.2019.08.008
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发表时间：
2019-09-03
期刊：
影响因子：
8.8
通讯作者：
Bassett, Danielle S.
中科院分区：
文献类型：
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作者：
Stiso, Jennifer;Khambhati, Ankit N.;Bassett, Danielle S.
由于对直接电刺激通过脑组织的物理传播的不完全理解，优化直接电刺激用于治疗神经系统疾病仍然很困难。在这里，我们使用网络控制理论来预测刺激如何通过白色物质传播，以影响空间分布的动态。我们测试的理论的预测使用一个独特的数据集，包括扩散加权成像和皮层脑电图癫痫患者接受网格刺激。我们发现统计上显着的预测活动状态转换和观察到的活动状态转换之间的共享方差。然后，我们使用一个最优控制框架来验证关于哪些大脑状态和结构特性在刺激时会有效地改善记忆编码的假设。我们的工作量化的作用，白色物质架构在指导直接电刺激的动力学和网络控制理论的效用，解释大脑的刺激反应提供了实证支持。
Optimizing direct electrical stimulation for the treatment of neurological disease remains difficult due to an incomplete understanding of its physical propagation through brain tissue. Here, we use network control theory to predict how stimulation spreads through white matter to influence spatially distributed dynamics. We test the theory's predictions using a unique dataset comprising diffusion weighted imaging and electrocorticography in epilepsy patients undergoing grid stimulation. We find statistically significant shared variance between the predicted activity state transitions and the observed activity state transitions. We then use an optimal control framework to posit testable hypotheses regarding which brain states and structural properties will efficiently improve memory encoding when stimulated. Our work quantifies the role that white matter architecture plays in guiding the dynamics of direct electrical stimulation and offers empirical support for the utility of network control theory in explaining the brain's response to stimulation.