Modeling the human brain as a network has been widely considered as a powerful approach to investigating the brain's structural and functional systems. However, many previous approaches focused on a single scale of brain network and the multi-scale nature of brain networks has been rarely explored yet. This paper put forward a novel framework to construct multi-scale common networks of brains via multi-scale spectral clustering of fiber connections among DICCCOLs. Specifically, the recently developed and publicly released DICCCOLs provide the nodal structural and functional correspondence across individuals, and thus the employed multi-scale spectral clustering algorithm divided the DICCCOL landmarks and their connections into sub-networks with correspondences on multiple scales. Experimental results showed the promise of the constructed multi-scale networks in applications of structural and functional connectivity mapping. As an application example, these multi-scale networks are used to guide the identification of multi-scale common fiber bundles across individuals and to facilitate the bundle's functional role analysis, which could enable other tract-based and network-based analyses in the future. © 2013 Springer-Verlag.
CITATION STYLE
Ge, B., Guo, L., Zhu, D., Zhang, T., Hu, X., Han, J., & Liu, T. (2013). Construction of multi-scale common brain networks based on DICCCOL. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7917 LNCS, pp. 692–704). https://doi.org/10.1007/978-3-642-38868-2_58
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