Failure of adaptive self-organized criticality during epileptic seizure attacks.
Failure of adaptive self-organized criticality during epileptic seizure attacks.
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DOI：
10.1371/journal.pcbi.1002312
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发表时间：
2012-01
影响因子：
4.3
通讯作者：
Gross T
中科院分区：
文献类型：
--
作者：
Meisel C;Storch A;Hallmeyer-Elgner S;Bullmore E;Gross T
临界动态被认为是对正常大脑功能有吸引力的模式，因为信息处理和计算能力在临界状态下是最佳的。最近对幂律分布（系统处于临界状态的标志）神经元活动模式的实验观察得出了这样的假设：人脑动力学可能处于有序和无序活动之间的相变状态。迄今为止尚未解决的问题涉及关键大脑活动的医学意义及其与病理状况的关系。利用人类侵入性脑电图记录的数据，我们发现在癫痫发作期间，神经元活动模式偏离了通常观察到的表征关键动态的幂律分布。将这些观察结果与基于自适应网络的自组织临界性 (SOC) 计算模型的结果进行比较，可以进一步深入了解潜在的动态。这些结果共同表明，由于适应性 SOC 失败而导致癫痫发作期间，大脑动力学偏离临界状态。近年来，很明显相变的概念不仅适用于物理学中经典考虑的系统。它适用于更广泛的一类复杂系统，这些系统表现出阶段，其特征是性质不同类型的长期行为。在直接位于过渡处的临界状态中，微小的变化可能对系统产生很大的影响。临界状态的这一特性和其他特性被证明对于计算和存储是有利的。因此，人们怀疑大脑神经网络的运行也接近临界状态。某些尺度关系的幂律的体外和体内测量支持了这一点，这些尺度关系是相变的标志。虽然临界动力学可以说是正常大脑功能的一种有吸引力的模式，但它与病理性大脑状况的关系仍未解决。在这里，我们展示了体内癫痫发作期间大脑动力学偏离临界状态。此外，计算模型的见解表明癫痫发作是由自适应自组织临界性失败引起的，这是一种基于网络动力学和拓扑之间相互作用的自组织临界性机制。
Critical dynamics are assumed to be an attractive mode for normal brain functioning as information processing and computational capabilities are found to be optimal in the critical state. Recent experimental observations of neuronal activity patterns following power-law distributions, a hallmark of systems at a critical state, have led to the hypothesis that human brain dynamics could be poised at a phase transition between ordered and disordered activity. A so far unresolved question concerns the medical significance of critical brain activity and how it relates to pathological conditions. Using data from invasive electroencephalogram recordings from humans we show that during epileptic seizure attacks neuronal activity patterns deviate from the normally observed power-law distribution characterizing critical dynamics. The comparison of these observations to results from a computational model exhibiting self-organized criticality (SOC) based on adaptive networks allows further insights into the underlying dynamics. Together these results suggest that brain dynamics deviates from criticality during seizures caused by the failure of adaptive SOC. Over the recent years it has become apparent that the concept of phase transitions is not only applicable to the systems classically considered in physics. It applies to a much wider class of complex systems exhibiting phases, characterized by qualitatively different types of long-term behavior. In the critical states, which are located directly at the transition, small changes can have a large effect on the system. This and other properties of critical states prove to be advantageous for computation and memory. It is therefore suspected that also cerebral neural networks operate close to criticality. This is supported by the in vitro and in vivo measurements of power-laws of certain scaling relationships that are the hallmarks of phase transitions. While critical dynamics is arguably an attractive mode of normal brain functioning, its relation to pathological brain conditions is still unresolved. Here we show that brain dynamics deviates from a critical state during epileptic seizure attacks in vivo. Furthermore, insights from a computational model suggest seizures to be caused by the failure of adaptive self-organized criticality, a mechanism of self-organization to criticality based on the interplay between network dynamics and topology.
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影响因子：
2.4
作者：
Meisel, Christian;Gross, Thilo
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