Investigating connectional characteristics of Motor Cortex network

Dong-Mei Hao, Ming-Ai Li

School of Electronic Information and Control Engineering, Beijing University of Technology.
School of Life Science and Bioengineering..

DOI: 10.4236/jbise.2009.21006                

Abstract

To understand the connectivity of cerebral cor-tex, especially the spatial and temporal pattern of movement, functional magnetic resonance imaging (fMRI) during subjects performing finger key presses was used to extract functional networks and then investigated their character-istics. Motor cortex networks were constructed with activation areas obtained with statistical analysis as vertexes and correlation coefficients of fMRI time series as linking strength. The equivalent non-motor cortex networks were constructed with certain distance rules. The graphic and dynamical measures of motor cor-tex networks and non-motor cortex networks were calculated, which shows the motor cortex networks are more compact, having higher sta-tistical independence and integration than the non-motor cortex networks. It indicates the motor cortex networks are more appropriate for information diffusion.

Keywords

Motor Cortex Network, Connectivity, Correlation Coefficient, Functional Magnetic Resonance Imaging (fMRI), Activation Area

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	V. M. Eguíluz, D. R. Chialvo, G. A. Cecchi et al. (2005) Scale-Free Brain Functional Networks. Physical Review Letters, 14 January: 018102-1~018102-4.
[2]	A. Riecker, D. Wildgruber, K. Mathiak et al. (2003) Parametric analysis of rate-dependent hemodynamic response functions of cortical and subcortical brain structures during auditorily cued finger tapping: a fMRI study. NeuroImage, 18: 731-739.
[3]	J. C. Zhuang, S. LaConte, S. Peltier, et al. (2005) Connectivity exploration with structural equation modeling: an fMRI study of bimanual motor coordination. NeuroImage, 25 (2): 462-470.
[4]	K. J. Friston, (2003) Dynamic causal modeling. NeuroImage, 19: 1273-1302.
[5]	L. Harrison, W. D. Penny, K. J. Friston, (2003) Multivariateautore-gressive modelling of fMRI time series. NeuroImage, 19: 1477–1491.
[6]	M. Eichler, (2005) A graphical approach for evaluating effective connectivity in neural systems. Philos. Trans. R. Soc. B, 360: 953-967.
[7]	S. Yang, D. Knoke. (2001) Optimal connections: strength and distance in valued graphs. Social Networks, 23: 285-295.
[8]	K. E. Stephan, C. C. Hilgetag, G. A. P. C. Burns et al. (2000) Computational analysis of functional connectivity between areas of primate cerebral cortex. Phil. Trans. R. Soc. Lond. B, 355, 111-126.
[9]	D. J. Felleman & D. C. Van Essen, (1991) Distributed hierar-chical processing in the primate cerebral cortex. Cereb Cortex, 1: 1-47.
[10]	S. Dodel, J. M. Herrmann, T. Geisel. (2002) Functional connec-tivity by cross-correlation clustering. Neurocomputing, 44- 46:1065-1070.
[11]	S. G. Tononi and G. M. Edelman. (2000) Theoretcal Neuro-anatomy: Relating Anatomical and Functional Connectivity in Graphs and Cortical Connection Matrices, Cerebral Cortex, 10: 127-141.
[12]	http://www.fmridc.org.
[13]	K. Y. Haaland, C. e L. Elsinger et al. (2004) Motor Sequence Complexity and Performing Hand Produce Differential Patterns of Hemispheric Lateralization. Journal of Cognitive Neurosci-ence, 16(4): 621-636.
[14]	http://www. analytictech.com.
[15]	A. McNamara, M. Tegenthoff, H. Dinse, et al. Increased func-tional connectivity is crucial for learning novel muscle synergies. Neuroimage, 2007, 35: 1211-1218 (p1213, ROI= 6mm, 7voxels).
[16]	http://www.talairach.org.
[17]	D. J. Serrien, R. B. Ivry, S. P. Swinnen. (2007) The missing link between action and cognition. Progress in Neurobiology, 82: 95-107.