Brain networks in multiple sclerosis and glioma: the road towards individualized care

Research output: PhD ThesisPhd-Thesis - Research and graduation internal


This thesis describes how the brain responds to damage in two neurological diseases. By studying both MS and glioma, it was found that although both diseases show specific characteristics, patterns of altered brain activity, connectivity and networks were partially overlapping, which increased our knowledge on the overall reaction of the brain to neurological damage and its relation to cognitive (dys)functioning. A wide range of methods were used to show alterations in brain activity, connectivity and networks in patients with MS and glioma that are clearly relevant for cognition. An important take home message that can be deducted from this thesis is that structural and functional connectivity do not quantify the same conceptual construct, while there is clinically relevant information in their (altered) interrelations. In MS, structural and functional damage should be considered as separately timed processes, where altered coupling between the two is related to cognitive impairment. In glioma, the tumor causes structural and functional alterations that can be seen throughout the brain, not only in the location of the tumor itself. More specifically, across disorders, it seems that activity seems to be affected mainly locally which may coincide with hyperconnectivity of specific regions, whereas functional network topology patterns seem to predominantly show more global differences when compared to healthy controls. Additionally, intrinsic brain network characteristics, i.e. brain network characteristics of healthy controls, may indicate disease severity which contributes to our understanding of the relationship between normal brain functioning and neurological diseases. Longitudinally, global functional network measures can provide additional predictive value for cognitive decline. Unfortunately, such longitudinal data remains rare, therefore computational modeling can be deployed when sufficiently optimized. By doing so, crucial information on which measures could be used to predict future cognitive decline can be gained and with that take some of the uncertainty away for patients that direly need some security with regard to their individual disease process.
Original languageEnglish
QualificationDoctor of Philosophy
  • Geurts, Jeroen Johan Guillaume, Supervisor, External person
  • Douw, Linda, Co-supervisor
  • Schoonheim, Menno Michiel, Co-supervisor, External person
Award date8 May 2023
Publication statusPublished - 8 May 2023

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