Network Analysis
Alex Fornito1
1Monash Institute of Cognitive and Clinical Neurosciences, Australia
Synopsis
This talk provides an introduction to network analysis of functional MRI, with an emphasis on the use of graph theory for understanding distinct aspects of brain organisation and dynamics.
Connectomics, graph theory and functional MRI
The brain is an extraordinarily complex network. It thus
stands to reason that methods for modelling the structure and dynamics of
networks should be useful for understanding the brain. Graph theory, a branch
of mathematics concerned with systems of interacting elements, provides a
natural formalism for modelling complex networks, including the brain, and rests
on the assumption that all such networks can be represented as a graph of nodes
connected by edges. The nodes represent fundamental processing units or
elements of the network; for example, in nervous systems, nodes could represent
individual neurons or large-scale brain regions. The edges represent some kind
of interaction between elements; for example, some measure of structural or
functional brain connectivity. Once the nodes and edges are defined, graph
theory offers a rich repertoire of diverse measures for quantifying different
aspects of network organization and function. In this talk, I will introduce
and motivate the use of graph theory for the analysis of functional MRI and discuss
critical issues associated with building a valid graph theoretic model of the
brain, with a focus on methods for defining nodes and edges. I will then
consider practical issues associated with analysing brain graphs, focusing on
the analysis of both network connectivity and topology, and provide example applications
at each step.Acknowledgements
No acknowledgement found.References
No reference found.
Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)