Epileptic seizures as extreme events in the human brain: Possibilities for prediction and prevention

• Applicant: Professor Dr. Klaus Lehnertz
• Subject Area: Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
• Term: from 2007 to 2014
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 61376376

• Final Report Year: 2014

Final Report Abstract

Epilepsy is one of the most common neurological disorders, affecting approximately 1 % of the world’s population. For about 25 % of patients, no sufficient treatment is currently available. One of the most devastating characteristics of epilepsy is the apparently unpredictable nature of seizures. A system able to forecast seizures far enough in advance to allow preventive action would reduce morbidity and mortality as well as greatly improve the quality of life of many people with epilepsy. World-wide research over the last twenty years has identified a number of analysis techniques that appear to be capable of identifying seizure precursors from the ongoing electroencephalogram. At present, however, none of these techniques achieves a performance that can be regarded as sufficient to provide clinically satisfactory solutions. The research we pursued during the previous funding period aimed at further improvements through (a) the development of more refined time series analysis techniques for the detection of seizure precursors and (b) through the development of computational models to deepen our understanding of mechanisms that underlie the initiation and termination of seizures in large-scale epileptic networks. With our time series analysis approaches that are based on concepts from statistical physics, synchronization theory, and network theory, we achieved a more detailed characterization of local and global properties of large-scale epileptic networks and their temporal evolutions. This allowed the identification not only of influencing factors that can hamper the detection of seizure precursors but also of network properties that may be useful for the development of novel, personalized treatment options. With our modeling approaches that make use of quite simple neuron models but complex connection topologies, we were able to generate a range of network dynamics similar to those seen in recordings of brain dynamics. Particularly, our model is able to self-generate and self-terminate recurrent events of synchrony, which resemble seizure-like activities. Our research sheds light onto the generating mechanisms as well as on stability, controllability and spread of such events. Although our findings can be regarded as promising to further improve both the detection of seizure precursors and our understanding of the seizuregenerating process, our research identified a number of open questions that require more in-depth studies.

Publications

• (2010): Long-term variability of global statistical properties of epileptic brain networks. Chaos 20: 043126
  Kuhnert MT, Elger CE, Lehnertz K
  
• (2011): Recurrent events of synchrony in a complex network of pulse-coupled oscillators. Europhysics Lett 95: 38001
  Rothkegel A, Lehnertz K
  
• (2011): Unraveling spurious properties of interaction networks with tailored random networks. PLoS ONE 6(8): e22826
  Bialonski S, Wendler M, Lehnertz K
  
• (2012): Identifying important nodes in weighted functional brain networks: A comparison of different centrality approaches. Chaos 22: 023142
  Kuhnert MT, Geier C, Elger CE, Lehnertz K
  
• (2012): Surrogate-assisted analysis of weighted functional brain networks. J Neurosci Methods 208: 165–172
  Ansmann G, Lehnertz K
  
• (2013): Assortative mixing in functional brain networks during epileptic seizures. Chaos 23: 033139
  Bialonski S, Lehnertz K
  
• (2013): Bursting and synchrony in networks of model neurons. In: Tetzlaff R, Elger CE, Lehnertz K (Eds.) Recent Advances in Predicting and Preventing Epileptic Seizures. World Scientific, Singapore, p. 108– 116
  Geier C, Rothkegel A, Elger CE, Lehnertz K
  
• (2013): Predictability of seizure-like events in a complex network model of integrate-and-fire neurons. In: Tetzlaff R, Elger CE, Lehnertz K (Eds.) Recent Advances in Predicting and Preventing Epileptic Seizures. World Scientific, Singapore, p. 99–107
  Rothkegel A, Lehnertz K
  
• (2014): Irregular macroscopic dynamics due to chimera states in small-world networks of pulse-coupled oscillators. New J Physics 16: 055006
  Rothkegel A, Lehnertz K
  
• How important is the seizure onset zone for seizure dynamics? Seizure, Volume 25, February 2015, Pages 160-166
  Geier C, Bialonski S, Elger CE, Lehnertz K