TY  - JOUR
A1  - Horstmann, Marie-Therese
A1  - Bialonski, Stephan
A1  - Noenning, Nina
A1  - Mai, Heinke
A1  - Prusseit, Jens
A1  - Wellmer, Jörg
A1  - Hinrichs, Hermann
A1  - Lehnertz, Klaus
T1  - State dependent properties of epileptic brain networks: Comparative graph–theoretical analyses of simultaneously recorded EEG and MEG
JF  - Clinical Neurophysiology
N2  - Objective
To investigate whether functional brain networks of epilepsy patients treated with antiepileptic medication differ from networks of healthy controls even during the seizure-free interval.

Methods
We applied different rules to construct binary and weighted networks from EEG and MEG data recorded under a resting-state eyes-open and eyes-closed condition from 21 epilepsy patients and 23 healthy controls. The average shortest path length and the clustering coefficient served as global statistical network characteristics.

Results
Independent on the behavioral condition, epileptic brains exhibited a more regular functional network structure. Similarly, the eyes-closed condition was characterized by a more regular functional network structure in both groups. The amount of network reorganization due to behavioral state changes was similar in both groups. Consistent findings could be achieved for networks derived from EEG but hardly from MEG recordings, and network construction rules had a rather strong impact on our findings.

Conclusions
Despite the locality of the investigated processes epileptic brain networks differ in their global characteristics from non-epileptic brain networks. Further methodological developments are necessary to improve the characterization of disturbed and normal functional networks.

Significance
An increased regularity and a diminished modulation capability appear characteristic of epileptic brain networks.
Y1  - 2010
U6  - http://dx.doi.org/10.1016/j.clinph.2009.10.013
SN  - 1388-2457
VL  - 121
IS  - 2
SP  - 172
EP  - 185
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Lehnertz, Klaus
A1  - Mormann, Florian
A1  - Osterhage, Hannes
A1  - Andy, Müller
A1  - Prusseit, Jens
A1  - Chernihovskyi, Anton
A1  - Staniek, Matthäus
A1  - Krug, Dieter
A1  - Bialonski, Stephan
A1  - Elger, Christian E.
T1  - State-of-the-art of seizure prediction
JF  - Journal of Clinical Neurophysiology
Y1  - 2007
U6  - http://dx.doi.org/10.1097/WNP.0b013e3180336f16
SN  - 1537-1603
VL  - 24
IS  - 2
SP  - 147
EP  - 153
ER  - 
TY  - JOUR
A1  - Lehnertz, Klaus
A1  - Bialonski, Stephan
A1  - Horstmann, Marie-Therese
A1  - Krug, Dieter
A1  - Rothkegel, Alexander
A1  - Staniek, Matthäus
A1  - Wagner, Tobias
T1  - Synchronization phenomena in human epileptic brain networks
JF  - Journal of neuroscience methods
Y1  - 2009
U6  - http://dx.doi.org/10.1016/j.jneumeth.2009.05.015
SN  - 0165-0270
VL  - 183
IS  - 1
SP  - 42
EP  - 48
ER  - 
TY  - JOUR
A1  - Geier, Christian
A1  - Lehnertz, Klaus
A1  - Bialonski, Stephan
T1  - Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing
JF  - Frontiers in Human Neuroscience
Y1  - 2015
U6  - http://dx.doi.org/10.3389/fnhum.2015.00462
SN  - 1662-5161
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  - 
TY  - JOUR
A1  - Bialonski, Stephan
A1  - Wendler, Martin
A1  - Lehnertz, Klaus
T1  - Unraveling spurious properties of interaction networks with tailored random networks
JF  - Plos one
N2  - We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.
Y1  - 2011
U6  - http://dx.doi.org/10.1371/journal.pone.0022826
VL  - 6
IS  - 8
PB  - Plos
CY  - San Francisco
ER  -