Format

Send to

Choose Destination
PLoS One. 2016 Apr 11;11(4):e0153105. doi: 10.1371/journal.pone.0153105. eCollection 2016.

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

Author information

1
Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University, Jena, Germany.
2
Institute of Anatomy I, Jena University Hospital, Friedrich Schiller University, Jena, Germany.
3
Department of Imaging Sciences, Department of Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, United States of America.

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 low- dimensional 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.

PMID:
27064897
PMCID:
PMC4827851
DOI:
10.1371/journal.pone.0153105
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center