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Voxelwise Spectral Diffusional Connectivity and Its Applications to Alzheimer’s Disease and Intelligence Prediction*
Junning Li1, Yan Jin1,2, Yonggang Shi1, Ivo D. Dinov1, Danny J. Wang3, Arthur W. Toga1, and Paul M. Thompson1, 2
1Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA 90095, USA
jli@loni.ucla.edu
yjin@loni.ucla.edu
yshi@loni.ucla.edu
idinov@loni.ucla.edu
toga@loni.ucla.edu
thompson@loni.ucla.edu
2Imaging Genetics Center, UCLA School of Medicine, Los Angeles, CA 90095, USA
3Brain Mapping Center, UCLA School of Medicine, Los Angeles, CA 90095, USA
jj.wang@loni.ucla.edu
Abstract. Human brain connectivity can be studied using graph theory. Many connectivity studies parcellate the brain into regions and count fibres extracted between them. The resulting network analyses require validation of the tractography, as well as region and parameter selection. Here we investigate whole brain connectivity from a different perspective. We propose a mathematical formulation based on studying the eigenvalues of the Laplacian matrix of the diffusion tensor field at the voxel level. This voxelwise matrix has over a million parameters, but we derive the Kirchhoff complexity and eigen-spectrum through elegant mathematical theorems, without heavy computation. We use these novel measures to accurately estimate the voxelwise connectivity in multiple biomedical applications such as Alzheimer’s disease and intelligence prediction.
*This work is supported by grants K01EB013633, R01MH094343, P41EB015922, RO1MH080892, R01EB008432, and R01EB007813 from NIH.
LNCS 8149, p. 655 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013