Defence of dissertation in the field of computer science, Onerva Korhonen, M.Sc.(Tech)

The linked brain: Looking for the definition of functional brain network nodes

Onerva Korhonen, M.Sc. (Tech) will defend the dissertation: “The quest for consistency: Effects of node definition and preprocessing on the structure of functional brain networks”.on 2 March 2018 at 12 o'clock at the Aalto University School of Science. In the dissertation, the candidate investigated methodological questions related to mapping the functional networks of the human brain. The results of the dissertation show that methods commonly used in network neuroscience may affect the results of network analysis in an unexpected way.

Network neuroscience models the human brain as a complex network, or as a system of nodes and links representing connections between the nodes. Although the network model has greatly increased our understanding on the structure and function of the human brain, a number of methodological questions of network neuroscience still remain unanswered. This dissertation concentrates on two of the questions: how should one define nodes of functional brain networks and how does preprocessing of the data affect the structure of the obtained networks. The results of the dissertation show that commonly used methods of network neuroscience have unexpected effects on the structure of the obtained networks. Further, these methods are partially based on insufficiently justified assumptions.

Regions of Interest (ROIs) are commonly used as nodes of functional brain networks. These ROIs consist of several measurement voxels of functional magnetic resonance imaging (fMRI) or source space vertices of electroencephalography (EEG) and magnetoencephalography (MEG). The ROI approach is based on the assumed functional homogeneity: each voxel or vertex of an ROI should behave similarly in time. However, according to the results of the dissertation, this assumption is not true for several sets of ROIs commonly used in fMRI, EEG, and MEG studies. Therefore, it is questionable if these ROIs are reasonable nodes for functional brain networks. Further, both functional homogeneity of ROIs and the local structure of functional brain networks change in time, which highlights the dynamic nature of functional brain networks. One may well ask if the optimal network model of the human brain can be based on any static set of nodes.

Making use of the full potential that methods of network science offer for neuroscience requires a solid methodological basis. The present dissertation wishes to warn about the consequences of careless methodological choices and to remind about how important the continuous, careful methodological work is for network neuroscience.

Dissertation release (pdf)

Opponent: Associate Professor Javier Martín Buldú, Center for Biomedical Technology, Madrid, Spain

Custos: ProfessorJari Saramäki, Aalto University School of Science, Department of Computer Science

Electronic dissertation: http://urn.fi/URN:ISBN:978-952-60-7843-4