Elisa Ryyppö (Aalto)
Wednesday 2018-05-30 14.00 – 14.30
Seminar room A346, T-building
Internal connectivity and topological roles of nodes in functional brain networks
Many real-life phenomena consist of a number of interacting elements and can thus be modeled as a complex network. The human brain is an example of such a system where the neuronal information processing of the brain is characterized by interaction and information change between different brain regions.
In this thesis, we examine functional brain networks estimated from functional magnetic resonance imaging (fMRI) data. In the analysis the small measurement units, voxels, are grouped into larger entities that represent supposedly functionally homogeneous brain regions referred to as Regions of Interest (ROIs). Despite their assumed homogeneity, it has been demonstrated that the voxels within a ROI exhibit spatially and temporally varying correlation structure. This gives rise to a concept referred to as internal connectivity.
On the larger scale, the ROIs form a brain network where each ROI has its role in the structure of the network topology, i.e. a topological role. Topological roles have been suggested to be indicative of the node’s functional specialization. On the other hand, it has been argued that internal connectivity may relate to the mechanisms the ROI uses to interact with its neighbors in the functional brain network. This thesis combines these two ideas. To this end, we aim to predict the ROI’s topological role from its internal connectivity features. We find that using internal connectivity features as model variables increases the classification accuracy in comparison to a baseline classifier.
These results suggest that there is a relationship between internal connectivity and the ROI’s topological role. This link provides a basis for faster and more computationally efficient topological role estimation. Further, it helps to better understand the mechanisms brain regions use to interact with each other. Both of these factors importantly increase our knowledge on brain function under different tasks and circumstances.