An Analytical Approach to Network Motif Detection in Samples of Networks with Pairwise Different Vertex Labels

Network motifs, overrepresented small local connection patterns, are assumed to act as functional meaningful building blocks of a network and, therefore, received considerable attention for being useful for understanding design principles and functioning of networks. We present an extension of the original approach to network motif detection in single, directed networks without vertex labeling to the case of a sample of directed networks with pairwise different vertex labels. A characteristic feature of this approach to network motif detection is that subnetwork counts are derived from the whole sample and the statistical tests are adjusted accordingly to assign significance to the counts. The associated computations are efficient since no simulations of random networks are involved. The motifs obtained by this approach also comprise the vertex labeling and its associated information and are characteristic of the sample. Finally, we apply this approach to describe the intricate topology of a sample of vertex-labeled networks which originate from a previous EEG study, where the processing of painful intracutaneous electrical stimuli and directed interactions within the neuromatrix of pain in patients with major depression and healthy controls was investigated. We demonstrate that the presented approach yields characteristic patterns of directed interactions while preserving their important topological information and omitting less relevant interactions

Schmidt C, Weiss T, Lehmann T, Witte H, Leistritz L (2013): Extracting labeled topological patterns from samples of networks. PLoS ONE 8:e70497.

<p>Effective connectivity network sample</p> <p> </p> <p>Schmidt C, Weiss T, Lehmann T, Witte H, Leistritz L (2013): Extracting labeled topological patterns from samples of networks. PLoS ONE 8:e70497.</p> <p> </p> <p>Schmidt C, Weiss T, Komusiewicz C, Witte H, Leistritz L (2012): An Analytical Approach to Network Motif Detection in Samples of Networks with Pairwise Different Vertex Labels 2012:1–12.</p> <p> </p

Multi-level characterization and information extraction in directed and node-labeled functional brain networks

Current research in computational neuroscience puts great emphasis on the computation and analysis of the functional connectivity of the brain. The methodological developments presented in this work are concerned with a group-specific comprehensive analysis of networks that represent functional interaction patterns. Four application studies are presented, in which functional brain network samples of different clinical background were analyzed in different ways, using combinations of established approaches and own methodological developments. Study I is concerned with a sample-specific decomposition of the functional brain networks of depressed subjects and healthy controls into small functionally important and recurring subnetworks (motifs) using own developments. Study II investigates whether lithium treatment effects are reflected in the functional brain networks of HIV-positive subjects with diagnosed cognitive impairment. For it, microscopic and macroscopic structural properties were analyzed. Study III explores spatially highly resolved functional brain networks with regard to a functional segmentation given by identified module (community) structure. Also, ground truth networks with known module structure were generated using own methodological developments. They formed the basis of a comprehensive simulation study that quantified module structure quality and preservation in order to evaluate the effects of a novel approach for the identification of connectivity (lsGCI). Study IV tracks the time-evolution of module structure and introduces a newly developed own approach for the determination of edge weight thresholds based on multicriteria optimization. The methodological challenges that underly these different topological analyses, but also the various opportunities to gain an improved understanding of neural information processing among brain areas were highlighted by this work and the presented results

ECN network samples - Extracting labeled topological patterns from samples of networks

<p>This is the raw data set (including missing values (-1)) I described and analyzed in my research papers:<br>++++<br>[1] C. Schmidt, T. Weiss, C. Komusiewicz, H. Witte, and L. Leistritz, “An Analytical Approach to Network Motif Detection in Samples of Networks with Pairwise Different Vertex Labels,” Computational and Mathematical Methods in Medicine, vol. 2012, no. 395, pp. 1–12, May 2012. http://doi.org/10.1155/2012/910380<br>++++<br>[2] C. Schmidt, T. Weiss, T. Lehmann, H. Witte, and L. Leistritz, “Extracting labeled topological patterns from samples of networks,” PLoS ONE, vol. 8, no. 8, p. e70497, 2013. http://doi.org/10.1371/journal.pone.0070497</p