A framework on surface-based connectivity quantification for the human brain

Hao Huang, Jerry L. Prince, Virendra Mishra, Aaron Carass, Bennett Landman, Denise C. Park, Carol Tamminga, Richard King, Michael I. Miller, Peter C.M. van Zijl, Susumu Mori

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Quantifying the connectivity between arbitrary surface patches in the human brain cortex can be used in studies on brain function and to characterize clinical diseases involving abnormal connectivity. Cortical regions of human brain in their natural forms can be represented in surface formats. In this paper, we present a framework to quantify connectivity using cortical surface segmentation and labeling from structural magnetic resonance images, tractography from diffusion tensor images, and nonlinear inter-subject registration. For a single subject, the connectivity intensity of any point on the cortical surface is set to unity if the point is connected and zero if it is not connected. The connectivity proportion is defined as the ratio of the total connected surface area to the total area of the surface patch. By nonlinearly registering the connectivity data of a group of normal controls into a template space, a population connectivity metric can be defined as either the average connectivity intensity of a cortical point or the average connectivity proportion of a cortical region. In the template space, a connectivity profile and a connectivity histogram of an arbitrary cortical region of interest can then be derived from these connectivity quantification values. Results from the application of these quantification metrics to a population of schizophrenia patients and normal controls are presented, revealing connectivity signatures of specified cortical regions and detecting connectivity abnormalities.

Original languageEnglish
Pages (from-to)324-332
Number of pages9
JournalJournal of Neuroscience Methods
Issue number2
StatePublished - Apr 30 2011

Bibliographical note

Funding Information:
This study was supported by NIH grants R01AG020012 , P41 RR015241 and R21 EB009545 . Peter van Zijl is a paid lecturer for Philips Medical Systems and has technology licensed to this company. This arrangement has been approved by Johns Hopkins University in accordance with its conflict of interest policy.


  • Connectivity
  • Cortical surface
  • DTI
  • Quantification
  • Tractography

ASJC Scopus subject areas

  • General Neuroscience


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