Multiple Sclerosis is the most common neurodegenerative and –inflammatory disease in young adults and leads to inflammatory lesions of the white matter that connects different brain regions and to a shrinkage of grey matter (brain atrophy). However, the relationship between these measures (e.g. lesion volume, brain atrophy) obtained through MR imaging and the actual clinical evolution is surprisingly low. An important possible mechanism for this clinico-radiological paradox is the idea that some lesions may affect more important pathways than others. In this research proposal, we propose to adjust and apply an existing model that allows to simulate MEG functional connectivity starting from a structural connectome and outputs a general connectivity coupling parameter and the conduction velocity. In the first work package, we will analyse differences in functional connectivity between MS patients and healthy controls. Next, we will do the same for structural connectivity. Finally, we will combine the two modalities (MEG and DTI) into the Kuramoto model. By explaining differences in MEG functional connectivity by differences in structural connectivity using a mechanistic model, we aim at (1) providing an entry point towards an improved understanding of one of the mechanisms leading to cognitive impairment in MS and (2) demonstrating the value of neuro-computational techniques to further our understanding of neurodegenerative/psychiatric disorders.
Effective start/end date1/10/1630/09/19

    Flemish discipline codes

  • Cognitive neuroscience

    Research areas

  • Multiple Sclerosis

ID: 25329338