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Interpretation of Neuroimaging Data Based on Network Concepts

Authors
  • McIntosh, Anthony R.1
  • Korostil, Michèle1, 2
  • 1 University of Toronto, Rotman Research Institute of Baycrest Centre, 3560 Bathurst Street, Toronto, ON, M6A 2E1, Canada , Toronto (Canada)
  • 2 University of Toronto, Centre for Addiction and Mental Health, 250 College St, Toronto, ON, M5S 2S1, Canada , Toronto (Canada)
Type
Published Article
Journal
Brain Imaging and Behavior
Publisher
Springer-Verlag
Publication Date
Aug 14, 2008
Volume
2
Issue
4
Identifiers
DOI: 10.1007/s11682-008-9031-6
Source
Springer Nature
Keywords
License
Green

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.

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