DocumentCode :
3506944
Title :
Structural connectivity via the tensor-based morphometry
Author :
Kim, Seung-Goo ; Chung, Moo K. ; Hanson, Jamie L. ; Avants, Brian B. ; Gee, James C. ; Davidson, Richard J. ; Pollak, Seth D.
Author_Institution :
Dept. of Brain & Cognitive Sci., Seoul Nat. Univ., Seoul, South Korea
fYear :
2011
fDate :
March 30 2011-April 2 2011
Firstpage :
808
Lastpage :
811
Abstract :
The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. We present a novel computational framework for investigating the white matter connectivity using TBM. Unlike other diffusion tensor imaging (DTI) based white matter connectivity studies, we do not use DTI but only T1-weighted magnetic resonance imaging (MRI). To construct brain network graphs, we have developed a new data-driven approach called the e-neighbor method that does not need any predetermined parcellation. The proposed pipeline is applied in detecting the topological alteration of the white matter connectivity in maltreated children.
Keywords :
biomedical MRI; brain; paediatrics; T1-weighted magnetic resonance imaging; brain network graph; diffusion tensor imaging; predetermined parcellation; structural connectivity; tensor-based morphometry; tissue volume difference; white matter connectivity; Biomedical imaging; Brain modeling; Correlation; Diffusion tensor imaging; Joining processes; Neuroscience; Jacobian determinant; brain network; maltreatment; structural connectivity; tensor-based morphometry;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
Conference_Location :
Chicago, IL
ISSN :
1945-7928
Print_ISBN :
978-1-4244-4127-3
Electronic_ISBN :
1945-7928
Type :
conf
DOI :
10.1109/ISBI.2011.5872528
Filename :
5872528
Link To Document :
بازگشت