Identifying directed links in large scale functional networks: Application to brain fMRI

Guillermo A. Cecchi*, A. Ravishankar Rao, Maria V. Centeno, Marwan Baliki, A. Vania Apkarian, Dante R. Chialvo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Background: Biological experiments increasingly yield data representing large ensembles of interacting variables, making the application of advanced analytical tools a forbidding task. We present a method to extract networks of correlated activity, specifically from functional MRI data, such that: (a) network nodes represent voxels, and (b) the network links can be directed or undirected, representing temporal relationships between the nodes. The method provides a snapshot of the ongoing dynamics of the brain without sacrificing resolution, as the analysis is tractable even for very large numbers of voxels. Results: We find that, based on topological properties of the networks, the method provides enough information about the dynamics to discriminate between subtly different brain states. Moreover, the statistical regularities previously reported are qualitatively preserved, i.e. the resulting networks display scale-free and small-world topologies. Conclusion: Our method expands previous approaches to render large scale functional networks, and creates the basis for an extensive and -due to the presence of mixtures of directed and undirected links- richer motif analysis of functional relationships.

Original languageEnglish (US)
Article numberS5
JournalBMC Cell Biology
Volume8
Issue numberSUPPL. 1
DOIs
StatePublished - Jul 10 2007

ASJC Scopus subject areas

  • Cell Biology

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