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.
Click here to participate in the public defence
Due to copyright reasons, an electronic copy of the thesis must be ordered from the faculty. In order for the faculty to have time to process the order, it must be received by the faculty no later than 2 days prior to the public defence. Orders received later than 2 days before the defence will not be processed. Inquiries regarding the thesis after the public defence must be addressed to the candidate.
Digital Trial Lecture – time and place
Adjudication committee
- First opponent: Assistant Professor Anna Rieckmann, Max Planck Institute for Social Law and Social Policy, Germany, and Umeå University, Sweden
- Second opponent: Assistant Professor Linda Geerligs, Radboud University, The Netherlands
- Third member and chair of the evaluation committee: Professor Pål Zeiner, University of Oslo
Chair of the Defence
Professor II Mathias Toft, University of Oslo
Principal Supervisor
Professor Tobias Kaufmann, University of Oslo
Summary
Effective communication between different parts of the brain is a key feature of normal brain function, and conditions that affect these brain networks can cause different symptoms and difficulties. The overarching aim of this doctoral thesis was to increase our understanding of healthy and abnormal connectome development across the lifespan, utilizing static and directed functional connectivity measures and pattern recognition tools on resting-state functional magnetic resonance imaging (fMRI) data.
Examining directed functional connectivity between brain networks using fMRI data from over 12,000 children to elderly individuals uncovered widespread differences in information flow in relation to age and between females and males. In addition, mapping the topological characteristics of the functional connectome revealed symptoms of depression and anxiety in adults to be associated with number of outgoing connections for visual, auditory, and sensorimotor networks.
We also explored functional brain age in two large samples of children and adolescents and its relation to mental health. Using a machine learning approach, we derived the apparent age of the brain - referred to as ‘brain age’ - and its deviation from chronological age, referred to as the ‘brain age gap’. Results showed that individuals with a lower estimated brain age had a higher overall mental health burden. However, these associations were not replicated in another sample and need further investigation in future studies.
New statistical methods allow us to investigate the extent to which developmental and age-related processes in the brain change the connection between networks, and how this is associated with mental health. Further research assessing validity and applying these tools in different age spans and samples may assist in producing characteristics of brain health and mental health maintenance that could help to inform in diagnostics and treatment response of mental disorders.
Additional information
Contact the research support staff.