Graph theory analysis of functional brain networks
Graph theory gives a language for networks. It allows to define networks and to quantify network properties at different levels. In general, networks (or graphs) are represented as sets of nodes N and edges k. The abstract representation of the brain as a graph allows to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Community structure analysis, which detects the groups of regions more densely connected between themselves than expected by chance, is essential for understanding brain network organization and topology .
Brain networks demonstrate hierarchical modularity, or multi-scale modularity, i.e. each module contains a set of sub-modules that contains a set of sub-sub-modules etc . We looked into that more closely.
- transcranial direct current stimulation (tDCS) and object recognition
Results: community structure changes depending on stimulation polarity and stimulation site (IPL: inferior parietal lobule, STS: superior temporal sulcus)
- real-time fMRI in pain processing
Results: Learners (upper row) have ideal community structure right from the start, non-Learners (lower row) do not - they have to acquire it during training (example shown: ACC upregulation)
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