Due to copyright issues, an electronic copy of the thesis must be ordered from the faculty. For the faculty to have time to process the order, the order must be received by the faculty at the latest 2 days before the public defence. Orders received later than 2 days before the defence will not be processed. After the public defence, please address any inquiries regarding the thesis to the candidate.
Trial Lecture – time and place
See Trial Lecture.
Adjudication committee
- First opponent: Associate Professor Menno Schoonheim, Amsterdam UMC
- Second opponent: Group Leader Katharina Fink, Karolinska Institutet
- Third member and chair of the evaluation committee: Professor Einar Osland Vik-Mo, University of Oslo
Chair of the Defence
Associate Professor Jon Ramm-Pettersen, University of Oslo
Principal Supervisor
Gro Owren Nygaard, Oslo University Hospital
Summary
Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease of the central nervous system, in which standard MRI markers often fail to capture the full complexity of ongoing tissue injury. This thesis explores the use of artificial intelligence and advanced quantitative imaging to develop more precise, tissuespecific biomarkers for assessing MS pathology. A deep learning model for brain age estimation was validated across a large multicenter MS cohort, demonstrating superior accuracy and reduced scanner bias compared to traditional machine learning. Building on this, paramagnetic rim lesions — markers of chronic smoldering inflammation detected by quantitative susceptibility mapping (QSM) — were found to associate with greater disability and accelerated brain aging even in early disease, suggesting that focal inflammatory activity contributes to neurodegeneration from the outset. QSM also revealed a progressive divergence from conventional FLAIR imaging with advancing age, captured by a novel metric termed the FLAIR–QSM discrepancy (ΔFQ), which we theorize reflects the growing burden of non-demyelinating, vascular-like white matter changes and was independently associated with clinical disability. Together, these findings support a shift toward personalized, biomarker-driven MS management. By integrating measures of global neurodegeneration, focal inflammation, and age-related white matter vulnerability, this work lays groundwork for more precise patient stratification, treatment monitoring, and ultimately, individualized care for people living with MS.
Additional information
Contact the research support staff.