Title :
Assessing graph models for description of brain networks
Author :
Yuan, Yixuan ; Guo, Lei ; Lv, Peili ; Hu, Xintao ; Zhang, Degang ; Han, Junwei ; Xie, Li ; Liu, Tianming
Author_Institution :
Sch. of Autom., Northwestern Polytech. Univ., Xi´´an, China
fDate :
March 30 2011-April 2 2011
Abstract :
Both structural and functional brain networks have been investigated in the literature with enthusiasm via graph-theoretical methods. However, an important issue that has not been adequately addressed before is: what is the optimal graph model for describing brain networks, both in structural and functional aspects? We address this question in the following three aspects. First, multi-resolution structural brain networks are reconstructed via cortical surface parcellation based on white matter fiber density information. Second, the global and local graph properties of the constructed networks are measured using state-of-the-art graph analysis algorithms and tools, and are further compared with five popular random graph models. Third, a functional simulation study is conducted to evaluate the synchronizability of the five models. Our results suggest that the STICKY graph model fits brain networks the best in terms of global and local graph properties, and the fastest speed of functional synchronization.
Keywords :
brain models; graph theory; STICKY graph model; functional brain network; structural brain network; surface parcellation; white matter fiber density information; Brain models; Data models; Diffusion tensor imaging; Indexes; Optical fiber networks; Synchronization; graph models; graph properties; multi-resolution structural brain networks;
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4244-4127-3
Electronic_ISBN :
1945-7928
DOI :
10.1109/ISBI.2011.5872532