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  - 
TY  - JOUR
A1  - Bialonski, Stephan
A1  - Lehnertz, Klaus
T1  - Assortative mixing in functional brain networks during epileptic seizures
JF  - Chaos: An Interdisciplinary Journal of Nonlinear Science
Y1  - 2013
U6  - http://dx.doi.org/10.1063/1.4821915
VL  - 23
IS  - 3
SP  - 033139
ER  - 
TY  - CHAP
A1  - Bialonski, Stephan
A1  - Lehnertz, Klaus
T1  - From time series to complex networks: an overview
T2  - Recent Advances in Predicting and Preventing Epileptic Seizures: Proceedings of the 5th International Workshop on Seizure Prediction
N2  - The network approach towards the analysis of the dynamics of complex systems has been successfully applied in a multitude of studies in the neurosciences and has yielded fascinating insights. With this approach, a complex system is considered to be composed of different constituents which interact with each other. Interaction structures can be compactly represented in interaction networks. In this contribution, we present a brief overview about how interaction networks are derived from multivariate time series, about basic network characteristics, and about challenges associated with this analysis approach.
Y1  - 2013
SN  - 978-981-4525-36-7
U6  - http://dx.doi.org/10.1142/9789814525350_0010
SP  - 132
EP  - 147
ER  -