Interpretation of Neuroimaging Data Based on Network Concepts

  • McIntosh A
  • Korostil M
  • 3


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


Abstract  By capturing the actions of distributed brain regions, neuroimaging can give unique insights into the networks underlying complex behavioral and cognitive functions. An approach to interpreting neuroimaging data grounded in emerging ideas in brain network theory is needed to better characterize these large-scale network dynamics. This paper focuses on three concepts germane to this approach to interpretation: “connectivity”, “neural context”, and “small-world properties”. Measures of brain connectivity emphasize the combined action of areas. Functional connectivity analyses focus on interacting neural patterns, whereas effective connectivity analyses uncover directional influences between brain areas. The second concept, neural context, purports that a region’s contribution to a function is more fully appreciated in relation to other coactive brain areas. The final concept is the extension of graph theory measures to the estimation of small-world properties. Measures such as clustering and path length can be used to infer the computational capacity of functional networks. These three constructs are central to the interpretation of neuroimaging data that will further unravel how brain network dynamics guide mental function, and are beginning to be applied to the study of neural disorders.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Anthony McIntosh

  • Michèle Korostil

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free