Modelling Human Cortical Network in Real Brain Space
ZHAO Qing-Bai1, FENG Hong-Bo1, TANG Yi-Yuan 1,2,3
1Institute of Neuroinformatics and Laboratory for Brain and Mind, Dalian University of Technology, Dalian 1160232Department of Psychology, University of Oregon, Eugene, OR 97403, USA3Key Laboratory for Mental Health, Chinese Academy of Sciences, Beijing 100101
Modelling Human Cortical Network in Real Brain Space
ZHAO Qing-Bai1;FENG Hong-Bo1;TANG Yi-Yuan 1,2,3
1Institute of Neuroinformatics and Laboratory for Brain and Mind, Dalian University of Technology, Dalian 1160232Department of Psychology, University of Oregon, Eugene, OR 97403, USA3Key Laboratory for Mental Health, Chinese Academy of Sciences, Beijing 100101
摘要Highly specific structural organization is of great significance in the topology of cortical networks. We introduce a human cortical network model, taking the specific cortical structure into account, in which nodes are brain sites placed in the actual positions of cerebral cortex and the establishment of edges depends on the spatial path length rather than the linear distance. The resulting network exhibits the essential features of cortical connectivity, properties of small-world networks and multiple clusters structure. Additionally, assortative mixing is also found in this model. All of these findings may be attributed to the specific cortical architecture.
Abstract：Highly specific structural organization is of great significance in the topology of cortical networks. We introduce a human cortical network model, taking the specific cortical structure into account, in which nodes are brain sites placed in the actual positions of cerebral cortex and the establishment of edges depends on the spatial path length rather than the linear distance. The resulting network exhibits the essential features of cortical connectivity, properties of small-world networks and multiple clusters structure. Additionally, assortative mixing is also found in this model. All of these findings may be attributed to the specific cortical architecture.
[1] Zemanova L, Zhou C and Kurths J 2006 Physica D 224 202 [2] Newman M E J 2003 SIAM Rev. 45 167 [3] Albert R and Barabasi A L 2002 Rev. Mod. Phys. 74 47 [4] Watts D J and Strogatz S H 1998 Nature 393 440 [5] Barabasi A L and Albert R 1999 Science 286 509 [6] Sporn O, Chialvo D R, Kaiser M and Hilgetag C C 2004 TrendsCogn. Sci. 8 418 [7] Kaiser M and Hilgetag C C 2004 Phys. Rev. E 69 036103 [8] Kaiser M and Hilgetag C C 2004 Neurocomputing 58-60 297 [9] Hilgetag C C, Burns G A, O'Neill M A, Scannell J W and Young M P2000 Philos. Trans. Roy. Soc. London B 355 91 [10] Scannell J W, Blakemore C and Young M P 1995 J. Neurosci. 15 1463 [11] Collins C L, Neelin P, Peters T M and Evans A C 1994 J.Comput. Assist. Tomogr 18 192 [12] Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, EtardO, Delcroix N, Mazoyer B and Joliot M 2002 NeuroImage 15273 [13] Hummel T,Damm M,Vent J,Schmidt M,Theissen P,Larsson M andKlussmannJ P 2003 Brain Res. 975 85 [14] Savel'ev V I 1984 Neurosci. Behav. Physiol. 14 60 [15] Ebeling U and Steinmetz H 1995 Acta Neurochir. Wien 136 8 [16] Humphries M D, Gurney K and Prescott T J 2006 Proc. R.Soc. B 273 503 [17] Stephan K E, Hilgetag C C, Burns G A, O'Neill M A, Young M P andK\"otter R 2000 Philos. Trans. Roy. Soc. London B 355 111 [18] Stam C J 2004 Neurosci. Lett. 355 25 [19] Salvador R, Suckling J, Coleman M, Pickard J D, Menon D K andBullmore E 2005 Cereb. Cortex 15 1332 [20] Achard S, Salvador R, Whitcher B, Suckling J and Bullmore E 2006 J. Neurosci. 26 63 [21] Sporns O and Zwi J D 2004 Neuroinformatics 2 145 [22] Latora V and Marchiori M 2001 Phys. Rev. Lett. 87198701 [23] Newman M E J and Girvan M 2004 Phys. Rev. E 69 026113 [24] Huang L, Park K, Lai Y C, Yang L and Yang K Q 2006 Phys. Rev.Lett. 97 164101 [25] Newman M E J 2002 Phys. Rev. Lett. 89 208701 [26] VHazquez A, Pastor-Satorras R and Vespignani A 2002 Phys.Rev. E 65 066130 [27] Jones S E, Buchbinder B R and Aharon I 2000 Hum. Brain Mapp. 11 12
2007, Vol. 24 
