Welcome to participate on the ISP-seminar where Nikolay Popov will present his project: Moment-to-moment brain signal variability in mental illness: functional magnetic resonance imaging investigations using naturalistic and task-based experimental designs
Brief description of the doctoral project
Social anxiety disorder (SAD) is considered to be a persistent debilitating condition characterised by marked and persistent fear of social or performance situations (Santomauro et al., 2021). It shares certain characteristics and is often comorbid with major depressive disorder. These and other psychiatric disorders are often diagnosed through clinical interview and with support from self-reported questionnaires such as the Liebowitz Social Anxiety Scale (LSAS), and Beck Depression Inventory (BDI). Such diagnostic methods often lack objectivity and there is a risk of incorrect diagnosis leading to subpar treatment outcomes, justifying the need for more objective biological measures. Functional magnetic resonance imaging (fMRI) is a non-invasive and powerful method to estimate neural responses in the brain and could be potentially used to improve patient outcomes. However, it is not used in diagnostics or treatment due to several limitations inherent to fMRI. Among other factors, inability to make accurate group-to-individual inferences and low test-retest reliability have limited the utility of both task and resting-state fMRI using standard measures (e.g., functional connectivity and average brain signals). In a recent paper, Månsson et al. (2022) demonstrated excellent 11-week test-retest reliability of temporal brain signal variability in patients with SAD. Although historically considered a source of noise, moment-to-moment brain signal variability continues to gain momentum as a sensitive and reliable indicator of individual differences in neural efficacy. A recent study also showed that brain signal variability could predict depression history with high accuracy. Nevertheless, most studies rely on cross-sectional data only and have not addressed longitudinal changes in brain signal variability. Such studies are needed to be able to interpret temporal changes in brain signals. This would potentially allow us to develop new biomarkers both for diagnostics and recovery progress tracking. Resting state fMRI (rs-fMRI) is often used in multi-center studies involving big sample size of participants (Tanaka et al., 2021) due to its ease of replication and rather simple analysis methods. Yet, as shown by Finn & Bandettini (2021) rs-fMRI is characterised by low prediction accuracy when linking brain and behavioural measures - and is inferior to task- and movie-watching fMRI. Latter uses naturalistic movie stimuli to extract brain measures. Studying brain signal variability from subjects exposed to naturalistic stimuli is a novel approach that has not yet been implemented in the clinical population and could potentially become a key to introducing fMRI into clinical practice.
Main supervisor: Kristoffer Månsson
Co-supervisor: Tie-Qiang Li
Email Barbara Efremius to participate, after that a Zoom-link will be sent out.