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A Novel Approach to Network Motif Detection Applied to the Analysis of Pain Processing Networks

Christoph Schmidt1*, Lutz Leistritz1, Thomas Weiß2, Jaroslav Ionov1, Karl J. Bär3 and Herbert Witte1
  • 1 Jena University Hospital -- Friedrich-Schiller-University Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Germany
  • 2 Friedrich-Schiller-University Jena, Department of Biological and Clinical Psychology, Germany
  • 3 University Hospital Jena, Department of Psychiatry and Psychotherapy, Germany

Connectivity analysis gains in importance for understanding cortical information processing. We used connectivity analysis to investigate EEG recorded processing of painful intracutaneous stimuli and directed interactions within the pain matrix in patients with major depression and healthy controls, by means of frequency selective generalized partial directed coherence (gPDC) [1]. The networks that originate from this analysis model pain processing in both groups and therefore represent important information for improving the currently inadequate understanding of the relationship between pain and depression. These pain processing networks (PPNs) cannot be readily interpreted because they exhibit dense and intricate patterns of directed interactions. To describe the local topology of PPN's of both groups we present a novel approach to network motif detection that extends the original approach of Milo et al. [2] to the case of sets of networks. Motifs are small, non-random subnetworks that are thought to act as functional meaningful building blocks of their network. A further novel aspect of our approach is taking into account the identifiers of employed EEG electrodes as vertex labels. In this way our approach preserves the anatomical and functional important positional information of motifs in the network. As a consequence, labeled motifs can be interpreted as patterns of characteristic directed interactions. We demonstrate that our motif detection approach is suitable to reveal different as well as identical patterns of characteristic directed interactions in the PPNs of patients with major depression and healthy controls while omitting unspecific interactions.

References

[1] L. Leistritz, T. Weiss ,J .Ionov, K. J. Bär, W. H. R. Miltner, and H. Witte. Connectivity analysis of somatosensory evoked potentials to noxious intracutaneous stimuli in patients with major depression. Methods Inf Med., 49(5):484–91, 2010.

[2] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, “Network motifs: simple building blocks of complex networks,” Science, vol. 298, no. 5594, p. 824, 2002.

Keywords: characteristic interactions, connectivity analysis, data analysis, Depression, graph theory, Network Motif, Pain

Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.

Presentation Type: Poster

Topic: data analysis and machine learning (please use "data analysis and machine learning" as keyword)

Citation: Schmidt C, Leistritz L, Weiß T, Ionov J, Bär KJ and Witte H (2011). A Novel Approach to Network Motif Detection Applied to the Analysis of Pain Processing Networks. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00208

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Received: 18 Aug 2011; Published Online: 04 Oct 2011.

* Correspondence: Mr. Christoph Schmidt, Jena University Hospital -- Friedrich-Schiller-University Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena, 07743, Germany, Christoph.Schmidt@mti.uni-jena.de