TY  - CONF
A1  - Wollstadt, Patricia
A1  - Vicente, Raul
A1  - Wibral, Michael
T1  - Graphical analyses in delay interaction networks
T2  - BMC neuroscience
N2  - Network or graph theory has become a popular tool to represent and analyze large-scale interaction patterns in the brain. To derive a functional network representation from experimentally recorded neural time series one has to identify the structure of the interactions between these time series. In neuroscience, this is often done by pairwise bivariate analysis because a fully multivariate treatment is typically not possible due to limited data and excessive computational cost. Furthermore, a true multivariate analysis would consist of the analysis of the combined effects, including information theoretic synergies and redundancies, of all possible subsets of network components. Since the number of these subsets is the power set of the network components, this leads to a combinatorial explosion (i.e. a problem that is computationally intractable). In contrast, a pairwise bivariate analysis of interactions is typically feasible but introduces the possibility of false detection of spurious interactions between network components, especially due to cascade and common drive effects. These spurious connections in a network representation may introduce a bias to subsequently computed graph theoretical measures (e.g. clustering coefficient or centrality) as these measures depend on the reliability of the graph representation from which they are computed. Strictly speaking, graph theoretical measures are meaningful only if the underlying graph structure can be guaranteed to consist of one type of connections only, i.e. connections in the graph are guaranteed to be non-spurious. ...
Y1  - 2013
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/31470
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-314709
UR  - https://link.springer.com/content/pdf/10.1186/1471-2202-14-S1-P413.pdf
SN  - 1471-2202
N1  - © 2013 Wollstadt et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
VL  - 14
IS  - (Suppl 1):P413
SP  - 1
EP  - 2
PB  - BioMed Central ; Springer
CY  - London ; Berlin ; Heidelberg
ER  -