These authors contributed equally to this work.
Investigation of the effective connectivity of resting state networks in Alzheimer's disease: a functional MRI study combining independent components analysis and multivariate Granger causality analysis
Article first published online: 16 APR 2012
Copyright © 2012 John Wiley & Sons, Ltd.
NMR in Biomedicine
Volume 25, Issue 12, pages 1311–1320, December 2012
How to Cite
Liu, Z., Zhang, Y., Bai, L., Yan, H., Dai, R., Zhong, C., Wang, H., Wei, W., Xue, T., Feng, Y., You, Y. and Tian, J. (2012), Investigation of the effective connectivity of resting state networks in Alzheimer's disease: a functional MRI study combining independent components analysis and multivariate Granger causality analysis. NMR Biomed., 25: 1311–1320. doi: 10.1002/nbm.2803
- Issue published online: 25 OCT 2012
- Article first published online: 16 APR 2012
- Manuscript Accepted: 8 MAR 2012
- Manuscript Revised: 1 MAR 2012
- Manuscript Received: 27 NOV 2011
- Alzheimer's disease;
- resting state functional MRI;
- effective connectivity;
- independent components analysis;
- multivariate Granger causality analysis
Recent neuroimaging studies have shown that the cognitive and memory decline in patients with Alzheimer's disease (AD) is coupled with abnormal functions of focal brain regions and disrupted functional connectivity between distinct brain regions, as well as losses in small-world attributes. However, the causal interactions among the spatially isolated, but functionally related, resting state networks (RSNs) are still largely unexplored. In this study, we first identified eight RSNs by independent components analysis from resting state functional MRI data of 18 patients with AD and 18 age-matched healthy subjects. We then performed a multivariate Granger causality analysis (mGCA) to evaluate the effective connectivity among the RSNs. We found that patients with AD exhibited decreased causal interactions among the RSNs in both intensity and quantity relative to normal controls. Results from mGCA indicated that the causal interactions involving the default mode network and auditory network were weaker in patients with AD, whereas stronger causal connectivity emerged in relation to the memory network and executive control network. Our findings suggest that the default mode network plays a less important role in patients with AD. Increased causal connectivity of the memory network and self-referential network may elucidate the dysfunctional and compensatory processes in the brain networks of patients with AD. These preliminary findings may provide a new pathway towards the determination of the neurophysiological mechanisms of AD. Copyright © 2012 John Wiley & Sons, Ltd.