PDF(Pubmed)
Abstract：
Epilepsy is defined by the abrupt emergence of harmful seizures, but the nature of these regime shifts remains enigmatic. From the perspective of dynamical systems theory, such critical transitions occur upon inconspicuous perturbations in highly interconnected systems and can be modeled as mathematical bifurcations between alternative regimes. The predictability of critical transitions represents a major challenge, but the theory predicts the appearance of subtle dynamical signatures on the verge of instability. Whether such dynamical signatures can be measured before impending seizures remains uncertain. Here, we verified that predictions on bifurcations applied to the onset of hippocampal seizures, providing concordant results from in silico modeling, optogenetics experiments in male mice and intracranial EEG recordings in human patients with epilepsy. Leveraging pharmacological control over neural excitability, we showed that the boundary between physiological excitability and seizures can be inferred from dynamical signatures passively recorded or actively probed in hippocampal circuits. Of importance for the design of future neurotechnologies, active probing surpassed passive recording to decode underlying levels of neural excitability, notably when assessed from a network of propagating neural responses. Our findings provide a promising approach for predicting and preventing seizures, based on a sound understanding of their dynamics.
摘要：
癫痫的定义是突然出现有害的癫痫发作，但是这些政权转变的性质仍然是神秘的。从动力系统理论的角度来看，在高度互连的系统中，这种临界转变发生在不明显的扰动上，并且可以建模为替代方案之间的数学分叉。关键过渡的可预测性是一个重大挑战，但是该理论预测了处于不稳定边缘的微妙动力学特征的出现。这种动态特征是否可以在即将发生的缉获之前进行测量仍然不确定。这里,我们验证了对分叉的预测适用于海马癫痫发作的开始，从计算机建模中提供一致的结果，雄性小鼠的光遗传学实验和人类癫痫患者的颅内脑电图记录。利用药理学控制神经兴奋性，我们发现,生理兴奋性和癫痫发作之间的界限可以从海马回路中被动记录或主动探测的动态特征推断出来.对未来神经技术的设计很重要，主动探测超过被动记录来解码潜在的神经兴奋性水平，特别是从传播神经反应的网络进行评估时。我们的发现为预测和预防癫痫发作提供了一种有希望的方法，基于对他们动态的良好理解。
