Single-Subject Structural Networks with Closed-Form Rotation Invariant Matching Improve Power in Developmental Studies of the Cortex

  • Benjamin M. Kandel
  • Danny JJ Wang
  • James C. Gee
  • Brian B. Avants
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)


Although much attention has recently been focused on single-subject functional networks, using methods such as resting-state functional MRI, methods for constructing single-subject structural networks are in their infancy. Single-subject cortical networks aim to describe the self-similarity across the cortical structure, possibly signifying convergent developmental pathways. Previous methods for constructing single-subject cortical networks have used patch-based correlations and distance metrics based on curvature and thickness. We present here a method for constructing similarity-based cortical structural networks that utilizes a rotation-invariant representation of structure. The resulting graph metrics are closely linked to age and indicate an increasing degree of closeness throughout development in nearly all brain regions, perhaps corresponding to a more regular structure as the brain matures. The derived graph metrics demonstrate a four-fold increase in power for detecting age as compared to cortical thickness. This proof of concept study indicates that the proposed metric may be useful in identifying biologically relevant cortical patterns.


Cortical Thickness Default Mode Network Cortical Structure Pediatric Data Graph Metrics 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Benjamin M. Kandel
    • 1
  • Danny JJ Wang
    • 2
  • James C. Gee
    • 1
  • Brian B. Avants
    • 1
  1. 1.Penn Image Computing and Science LaboratoryUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of NeurologyUniversity of CaliforniaLos AngelesUSA

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