%0 Journal Article %1 Meisel2012a %A Meisel, Christian %A Storch, Alexander %A Hallmeyer-Elgner, Susanne %A Bullmore, Ed %A Gross, Thilo %D 2012 %J PLoS Comput Biol %K adaptive-networks brain epilepsy networks neural-network neuronal-networks neuroscience self-organized-criticality soc %N 1 %P e1002312 %R 10.1371/journal.pcbi.1002312 %T Failure of Adaptive Self-Organized Criticality during Epileptic Seizure Attacks %V 8 %X 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.

@article{Meisel2012a, abstract = {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.}, added-at = {2012-07-13T11:21:46.000+0200}, author = {Meisel, Christian and Storch, Alexander and Hallmeyer-Elgner, Susanne and Bullmore, Ed and Gross, Thilo}, biburl = {https://www.bibsonomy.org/bibtex/2344bf654358596dcfcc6fa75539ebb1b/rincedd}, doi = {10.1371/journal.pcbi.1002312}, interhash = {254f6bc59f854bafe36cd3cb842ceca6}, intrahash = {344bf654358596dcfcc6fa75539ebb1b}, journal = {PLoS Comput Biol}, keywords = {adaptive-networks brain epilepsy networks neural-network neuronal-networks neuroscience self-organized-criticality soc}, number = 1, pages = {e1002312}, timestamp = {2012-07-13T11:21:46.000+0200}, title = {Failure of Adaptive Self-Organized Criticality during Epileptic Seizure Attacks}, volume = 8, year = 2012 }