Public defence: Sandra Edding Klonteig
Sandra Edding Klonteig will defend her thesis “Technological Approaches to Measure Tendencies in Brain Function with Relevance to Mental Health” for the PhD in Engineering Science.
Sandra Edding Klonteig will defend her thesis “Technological Approaches to Measure Tendencies in Brain Function with Relevance to Mental Health” for the PhD in Engineering Science.
The trial lecture and public defence will also be streamed live:
Ordinary opponents:
Leader of the public defence: Head of Studies Simen Gjelseth Antonsen, OsloMet
Main supervisor: Professor Rune Jonassen, OsloMet
Co-supervisors:
Mental disorders have been linked to systematic deviations, or tendencies, in brain function and activity patterns. Advancing the understanding and quantification of such tendencies may support the development of more targeted and effective treatments. Technologies like electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and functional near infrared spectroscopy (fNIRS) are used to indirectly measure brain activity. In addition, more peripheral measures, such as eye tracking and reaction time (RT) monitoring, are used to measure physiological and behavioral responses that may reflect underlying brain functions.
Despite the potential of these approaches, obtaining reliable and valid measures of tendencies in brain function is a challenge, and further mechanistic insight is needed. There is also a need to consider how these technologies can be applied across research and clinical contexts, including trade-offs related to measurement quality, practical implementation, and accessibility.
This thesis aims to examine how various types of technology can be used to measure relevant tendencies in brain function, with the ultimate goal of leading to innovations in clinical mental health practice. It focuses on select aspects, such as reliability, validity, feasibility, and clinical relevance, of specific methods. Recognizing that no single technique captures the full complexity of the brain, another aim is to explore the potential of combining multiple techniques for a more comprehensive understanding of brain function.
The thesis focuses on two tendencies in brain function that are assumed to be relevant to mental health. The first is attentional bias (AB), such as a tendency to focus on negative aspects of a situation. The second is aberrations in functional networks, or differences in how brain regions interact, which are linked to various cognitive and emotional functions. Four main technologies—eye tracking, EEG, fMRI, and fNIRS—are studied in combination with behavioral and psychological measures.
Study I piloted an experimental setup to measure AB using separate recordings of eye tracking, EEG, fNIRS, and a response box (for RT monitoring) during a behavioral task. It also explored the potential of combining techniques into a single integrated system.
Study II examined the reliability and validity of a range of AB metrics using a combined setup involving the response box, eye tracking, and EEG. It evaluated the feasibility of using this integrated system to measure AB as well.
Study III used the most reliable AB metric from Study II to explore further relevance for mental health. Specifically, it explored links between AB and the use of over-the-counter analgesics (OTCA), which may be an expression of how mental health symptoms are managed through behavior.
Study IV used resting-state fMRI to examine functional connectivity in patients receiving antidepressant treatment and a control group. Resting state networks (RSNs) were compared between groups at baseline, followed by an analysis of whether changes in RSNs reflect the treatment-related effects of antidepressant medication.
Study I involved six participants and resulted in a piloted design for Studies II and III, where eye tracking, EEG, and a response box were combined into one integrated system to measure AB in 62 young females.
Study II yielded the most reliable AB metrics, with the specific setup, to derive from RT monitoring, followed by eye tracking and EEG. No relationship between the AB metrics and symptoms of depression and anxiety were found. Challenges with the integrated system were also highlighted, and adjustments for future studies were proposed.
Study III indicated preliminary associations between frequent OTCA use and AB, suggesting further directions for future studies.
Study IV revealed no significant RSN differences between 40 patients and 39 healthy controls at baseline. However, results showed longitudinal RSN changes potentially linked to antidepressant effects. No associations to changes in symptoms were found.
Overall, the four studies underscore both the potential and limitations of using technology to measure relevant tendencies in brain function. The findings highlight important strengths and challenges involved in using different technologies, as well as practical considerations and trade-offs relevant to future research and clinical use, and underscore the need for continued methodological refinement, multimodal integration, and improved usability to support broader applications.