The brain’s functional and structural network architecture and its relationship to cognition and CNS disease.
To a large extent, we use data acquired using functional Magnetic Resonance Imaging (fMRI), but also from Magnetoenephalography (MEG) and Electroencephalography (EEG). Network-based models are built, tested and validated to be able to follow dynamical events in the brain during rest as well as for cognitive tasks. We collaborate closely with other research groups to study neurological conditions from a network-based perspective. The overall aim of our research is to provide knowledge based on network-based models that significantly contribute to the diagnosis of diseases in the CNS as well as an increase in our understanding of how our brain can be viewed and studied as a highly complex, hierarchical and dynamical biological system.
We apply functional connectivity MRI, structural connectivity (diffusion-weighted) MRI as well as EEG/MEG methods to study the hierarchical network organization of the human brain at rest as well as during task performance. In particular, we perform research to understand the interaction between the ubiquitous spontaneous oscillations in the brain and its relation to variability in one's state of mind and task performance.
Together with collaborative partners at the Karolinska Hospital, we apply a network approach to study the effects of brain trauma as well as neurodevelopmental disorders on the human connectome. The developmental of cortical networks is a central theme in our research and we were the first group to show the functional large-scale cortical network architecture in the infant human brain.
- Modelling of brain activity based on hierarchical and spatiotemporally flexible subnetworks.
- Cognition and the dynamics of the brain’s network structure.
- Network-based modelling of brain activity related to epilepsy.
- Relationship between chronic pain and changes in the brain’s dynamical networks.
- Extreme premature birth and its connection to changes in the brain’s functional networks