Identification of whole-brain network modules based on a large scale Granger Causality approach

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung


  • Britta Pester - , Friedrich-Schiller-Universität Jena (Autor:in)
  • Christoph Schmidt - , Friedrich-Schiller-Universität Jena (Autor:in)
  • Nicole Schmid-Hertel - , Friedrich-Schiller-Universität Jena (Autor:in)
  • Herbert Witte - , Friedrich-Schiller-Universität Jena (Autor:in)
  • Axel Wismueller - , University of Rochester (Autor:in)
  • Lutz Leistritz - , Friedrich-Schiller-Universität Jena (Autor:in)


Spatially high resolved neurophysiological data commonly pose a computational and analytical problem for the identification of functional networks in the human brain. We introduce a multivariate linear Granger Causality approach with an embedded dimension reduction that enables the computation of brain networks at the large scale. In order to grasp the information about connectivity patterns contained in the resulting high-dimensional directed networks, we furthermore propose the inclusion of module detection methods from network theory that can help to identify functionally associated brain areas. As a proof of concept, the methodology is verified by means of synthetic data with known ground truth module properties. Resting state fMRI data are used to demonstrate the applicability and benefit in the case of clinical data.


Titel2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Herausgeber (Verlag)IEEE, New York [u. a.]
ISBN (elektronisch)9781424492718
PublikationsstatusVeröffentlicht - 4 Nov. 2015
Extern publiziertJa


Titel37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
KurztitelEMBC 2015
Dauer25 - 29 August 2015

Externe IDs

PubMed 26737507
ORCID /0000-0001-8264-2071/work/142254068