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  - 
TY  - JOUR
A1  - Kuhnert, Marie-Therese
A1  - Bialonski, Stephan
A1  - Noenning, Nina
A1  - Mai, Heinke
A1  - Hinrichs, Hermann
A1  - Helmstaedter, Christoph
A1  - Lehnertz, Klaus
T1  - Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks
JF  - Plos one
N2  - Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.
Y1  - 2013
U6  - http://dx.doi.org/10.1371/journal.pone.0080273
VL  - 8
IS  - 11
PB  - PLOS
CY  - San Francisco
ER  -