Graph theoretical analysis of structural brain connectivity

from diffusion tensor image (DTI)

October 23, 2012, 2012 4:00 PM

302- 308



Understanding neural connectivity pattern of a brain has attracted neuroscientists longer than a century (Cajal, 1909, Brodmann, 1909). Diffusion weighted MRI, which is a rather newly developed MR technique, becomes a popular method to research the white matter integrity of human brains, since it was introduced in 1980s; especially, diffusion tensor imaging (DTI, Moseley et al, 1990) is the first systematical and simple approach. It models each voxels diffusion as a tensor, an ellipsoidal shape with 3 major diffusion directions and their magnitudes. Computational neural tract tracing based on this directional information of each voxel through fiber tracking methods (Mori et al., 1999; Behrens et al., 2003) may provide structural (anatomical) connectivity of brains. Structural connectivity is more direct evidence of neural connection between various brain regions while functional connectivity based on functional MRI or EEG only represents statistical dependency between brain regions. Compared to the previous tract tracing techniques like fluorescence tracers which also reveal structural connectivity, MR image is non-invasive and enables studying human brains. Consequently, DTI has been successful in researching neural disorders, which is rather limited in animal studies. Combined with the graph theory (Watts & Strogatz 1998, Barabasi & Albert 1999), brain connectivity analysis reveals interesting aspects of brain organization, including small-world characteristics, and hierarchical modular structure. Also these network measures may characterize neural disorders or developmental changes; in examples, decreased local efficiency and modularity over development, and altered network properties in SchizophreniaThis page is maintained by Yumi Yi (
Last update: October 30, 2012