A multivariate Granger Causality concept towards full brain functional connectivity

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Christoph Schmidt - , Friedrich Schiller University Jena (Joint first author)
  • Britta Pester - , Friedrich Schiller University Jena (Joint first author)
  • Nicole Schmid-Hertel - , Friedrich Schiller University Jena (Author)
  • Herbert Witte - , Friedrich Schiller University Jena (Author)
  • Axel Wismuller - , University of Rochester (Author)
  • Lutz Leistritz - , Friedrich Schiller University Jena (Author)

Abstract

Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially lowdimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

Details

Original languageEnglish
Article numbere0153105
JournalPloS one
Volume11
Issue number4
Publication statusPublished - Apr 2016
Peer-reviewedYes
Externally publishedYes

External IDs

PubMed 27064897
ORCID /0000-0001-8264-2071/work/142254074

Keywords

ASJC Scopus subject areas