Construction of multi-scale common brain networks based on DICCCOL

  • Authors:

  • Bao Ge;Lei Guo;Dajiang Zhu;Tuo Zhang;Xintao Hu;Junwei Han;Tianming Liu

  • Affiliations:

  • College of Physics & Information Technology, Shaanxi Normal University, Xi'an, China;School of Automation, Northwestern Polytechnical University, Xi'an, China;Cortical Architecture Imaging and Discovery Lab, Department of Computer Science, University of Georgia, Athens, GA;School of Automation, Northwestern Polytechnical University, Xi'an, China,Cortical Architecture Imaging and Discovery Lab, Department of Computer Science, University of Georgia, Athens, GA;School of Automation, Northwestern Polytechnical University, Xi'an, China;School of Automation, Northwestern Polytechnical University, Xi'an, China;Cortical Architecture Imaging and Discovery Lab, Department of Computer Science, University of Georgia, Athens, GA

  • Venue:
  • IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
  • Year:
  • 2013

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Abstract

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.