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海马体癫痫的临界动力学。

The critical dynamics of hippocampal seizures.

机构信息

Center for experimental neurology, Sleep-wake epilepsy center, NeuroTec, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland.

Department of Neurosurgery, Inselspital, University Hospital Bern, University of Bern, Bern, Switzerland.

出版信息

Nat Commun. 2024 Aug 13;15(1):6945. doi: 10.1038/s41467-024-50504-9.

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

癫痫的定义是有害癫痫发作的突然出现,但这些状态转变的性质仍然是神秘的。从动力系统理论的角度来看,这种关键转变发生在高度互联系统中不起眼的扰动下,可以建模为替代状态之间的数学分岔。关键转变的可预测性是一个主要挑战,但该理论预测了在不稳定的边缘出现微妙的动力学特征。这些动力学特征是否可以在即将发生癫痫之前进行测量仍然不确定。在这里,我们验证了对分岔的预测适用于海马体癫痫发作的发作,提供了来自计算机模拟、雄性小鼠光遗传学实验和癫痫患者颅内 EEG 记录的一致结果。利用对神经兴奋性的药理学控制,我们表明可以从海马体电路中被动记录或主动探测的动力学特征推断生理兴奋性和癫痫发作之间的边界。对于未来神经技术的设计来说,主动探测优于被动记录,以解码潜在的神经兴奋性水平,尤其是从传播的神经反应网络进行评估时。我们的研究结果为预测和预防癫痫提供了一种有前途的方法,这是基于对其动力学的深入理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b274/11322644/3a9a7f1a7209/41467_2024_50504_Fig1_HTML.jpg

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