Digital Public Defence: Einar August Høgestøl Einar August Høgestøl at Institute of Clinical Medicine will be defending the thesis MRI and Other Biomarkers in Early MS for the degree of PhD (Philosophiae Doctor).

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Photo: Amalie Huth Hovland, UiO

The public defence will be held as a video conference over Zoom.

The defence will follow regular procedure as far as possible, hence it will be open to the public and the audience can ask ex auditorio questions when invited to do so.

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Digital Trial Lecture - time and place

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Adjudication committee

  • First opponent: Associate Professor Hugo Vrenken, Vrije Universiteit, Amsterdam
  • Second opponent: Professor Øivind F. Grytten Torkildsen, University of Bergen, Norway
  • Third member and chair of the evaluation committee: Professor II Angelika Gabriele Sorteberg, Institute of Clinical Medicine, University of Oslo 

Chair of defence

Professor Trygve Holmøy, Institute of Clinical Medicine, University of Oslo 

Principal Supervisor

Professor II Hanne Flinstad Harbo, Institute of Clinical Medicine, University of Oslo


In multiple sclerosis (MS), magnetic resonance imaging (MRI) has become an essential part of the diagnostic process and in the follow-up of MS-patients. The technological advances in recent decades have enabled researchers and clinicians to exploit the data acquired from MRI scans of the human brain.

The aim of the thesis was to utilize the MRI data from a local prospective five year longitudinal MS cohort, using state-of-the-art methods, to give new insights into disease pathophysiology and possibly serve as an imaging marker for disease activity.

First, we found that increased connectivity in the default mode network (DMN) in the brain was associated with increased levels of fatigue and depressive symptoms. When disentangling the symptoms using a principal components analysis, we found a subgroup of MS patients with a high level of fatigue and a low level of depressive symptoms that was associated with increased DMN connectivity.

Using MRI data from our longitudinal MS-dataset, we adapted a machine learning method able to predict an individual brain age using MRI brain scans from 3208 healthy controls. The brains of the MS patients were estimated to be on average 4,4 years older than their chronological age, while their brains also exhibited a 41 % accelerated rate of brain aging compared to the expected rate. The MS patients were clinically stable during the follow-up. 
We also investigated the cognitive performance of the MS patients which on average was stable or increased during the follow-up. Worse performance in one test for processing speed (Color-Word Interference Test) was significantly associated with increased brain age.
This theis explored modern applications of MRI data to investigate novel imaging markers in MS. We found several potential imaging markers using our local MS sample, giving rise to ideas for future research projects.

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Published Oct. 13, 2020 1:15 PM - Last modified Oct. 28, 2020 9:20 AM