Structural connectivity via the tensor-based morphometry

Kim, Seung-Goo; Chung, Moo K.; Hanson, Jamie; Avants, Brian B.; Gee, James C.; Davidson, Richard J.; Pollak, Seth D.

doi:10.1109/ISBI.2011.5872528

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Structural connectivity via the tensor-based morphometry

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Kim, S.-G., Chung, M. K., Hanson, J., Avants, B. B., Gee, J. C., Davidson, R. J., et al. (2011). Structural connectivity via the tensor-based morphometry. In Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 808-811).

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F907-0

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 ε-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.