Construction of multi-scale brain networks via DICCCOL landmarks

Mapping human brain networks has gained significant interest in the last few years, as it offers novel perspectives on the brain structure and function. However, most previous approaches were dedicated to a single resolution or scale of brain network, though the brain networks are multi-scale in nature. This paper presents a novel approach to constructing multi-scale structural brain networks from DTI images via multi-scale spectral clustering of our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess structural and functional correspondences across subjects, the constructed multi-scale networks also have intrinsically-established correspondences across different brains, which is a prominent feature of this network construction method. Experimental results demonstrated that the proposed method can generated consistent and common structural brain networks, which will lay down the foundation for many other network-based neuroimaging analyses in the future.

[1]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[2]  Stephen T. C. Wong,et al.  A hybrid approach to automatic clustering of white matter fibers , 2010, NeuroImage.

[3]  Edward T. Bullmore,et al.  Whole-brain anatomical networks: Does the choice of nodes matter? , 2010, NeuroImage.

[4]  Zoubin Ghahramani,et al.  Spectral Methods for Automatic Multiscale Data Clustering , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Steven J. Gortler,et al.  Fast exact and approximate geodesics on meshes , 2005, ACM Trans. Graph..

[6]  D. Shen,et al.  DICCCOL: dense individualized and common connectivity-based cortical landmarks. , 2013, Cerebral cortex.