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Analyzing brain networks with PCA and conditional Granger causality.

Authors
  • Zhou, Zhenyu
  • Chen, Yonghong
  • Ding, Mingzhou
  • Wright, Paul
  • Lu, Zuhong
  • Liu, Yijun
Type
Published Article
Journal
Human Brain Mapping
Publisher
Wiley (John Wiley & Sons)
Publication Date
Jul 01, 2009
Volume
30
Issue
7
Pages
2197–2206
Identifiers
DOI: 10.1002/hbm.20661
PMID: 18830956
Source
Medline
License
Unknown

Abstract

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series.

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