Published in
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
DOI: 10.1109/isbi.2011.5872528
Links
- ORCID
- ORCID
- Institute of Electrical and Electronics Engineers
- [www.ncbi.nlm.nih.gov] | PDF
- [hdl.handle.net] | PDF
- [europepmc.org] | PDF
- [www.researchgate.net] | PDF
- [www.ncbi.nlm.nih.gov] | PDF
- [citeseerx.ist.psu.edu] | PDF
- [www.stat.wisc.edu] | PDF
- [pubman.mpdl.mpg.de] | PDF
Tools
Structural connectivity via the tensor-based morphometry
,
Brian B. Avants,
James C. Gee,
Richard J. Davidson ,
Seth D. Pollak
Full text: Download
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.