Effective connectivity of brain network based on granger causality and PCA

被引：0

作者：

Zhong Y. ^{[1
]}

Wang H.-N. ^{[1
]}

Jiao Q. ^{[2
]}

Zhang Z.-Q. ^{[1
]}

Zheng G. ^{[1
]}

Yu H.-Y. ^{[1
]}

Lu G.-M. ^{[2
]}

机构：

[1] College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu

[2] Department of Medical Imaging, Nanjing General Hospital of Nanjing Military Command of PLA, Nanjing 210002, Jiangsu

来源：

Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2010年 / 38卷 / 01期

关键词：

Effective connectivity; Functional magnetic resonance imaging; Granger causality; Principal component analysis;

D O I：

10.3969/j.issn.1000-565X.2010.01.015

中图分类号：

学科分类号：

摘要：

In order to improve the detection reliability of effective connectivity in brain network, an fMRI (Functional Magnetic Resonance Imaging) analytical approach of effective connectivity is proposed based on the Granger causality (GC) and the principle component analysis (PCA). In this approach, first, temporal principal components are extracted via the PCA from the fMRI signals in the region of interest, and the patterns are considered as temporal reference information. Next, the Granger causality between the reference region and each of other voxels of the brain is calculated. Then, the results are mapped into the whole brain and a Granger causality map (GCM) is thus obtained. Moreover, a theoretical derivation is performed to verify the effectiveness of the proposed approach. The proposed approach is finally used to analyze the GCM of a manual movement task-induced activation in the motor area, the results verifying the correctness of theory of motor-function neural network.

引用

页码：76 / 80

页数：4

共 16 条

[1] Friston K., Frith C., Frackowiak R., Time-dependent changes in effective connectivity measured with PET, Human Brain Mapping, 1, 1, pp. 69-80, (1993)
[2] Buchel C., Friston K.J., Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI, Cereb Cortex, 7, 8, pp. 768-778, (1997)
[3] Friston K., Mechelli A., Turner R., Et al., Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics, Neuroimage, 12, 4, pp. 466-477, (2000)
[4] Goebel R., Roebroeck A., Kim D., Et al., Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping, Magnetic Resonance Imaging, 21, 10, pp. 1251-1261, (2003)
[5] Marreiros A., Kiebel S., Friston K., Dynamic causal modelling for fMRI: a two-state model, Neuroimage, 39, 1, pp. 269-278, (2008)
[6] Bressler S., Tang W., Sylvester C., Et al., Top-down control of human visual cortex by frontal and parietal cortex in anticipatory visual spatial attention, Journal of Neuroscience, 28, 40, pp. 10056-10061, (2008)
[7] Jiao Q., Zhong Y., Guo Y.-X., Et al., Functional magnetic resonance imaging study on medial temporal lobe epileptic activities using Granger causality, Chinese Journal of Biomedical Engineering, 28, 1, pp. 7-11, (2009)
[8] Gao Q., Chen H.-F., Gong Q.-Y., Evaluation of the effective connectivity of the dominant primary motor cortex during bimanual movement using Granger causality, Neuroscience Letters, 443, 1, pp. 1-6, (2008)
[9] Granger C., Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 37, 3, pp. 424-438, (1969)
[10] Granger C., Testing for causality: a personal viewpoint, Journal of Economic Dynamics and Control, 2, 1, pp. 329-352, (1980)

← 1 2 →