Date of Award
Summer 8-9-2023
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
In our everyday lives, we experience several life events that are either positive in nature (uplifting events) or irritating in nature (hassling events). Independently, these events may not impact an individual’s mood, but together they may accumulate to do so. As such, one would hypothesize that a greater number of hassles experienced would lead to a higher depression level, whilst a greater number of uplifts experienced would lead to a lower depression level. The first Aim of this thesis is to investigate the association between cumulative life events and depression using a linear regression model. The findings revealed that life events (combined cumulative hassles and uplifts) explain 31% of the variance in depression scores, with uplifts related to the ability to confide and health-related hassles being the greatest contributors in relation to the proportion of lower and higher depression levels, respectively. This study confirms the importance of daily life experience in the development of psychopathology. By identifying significant associations between life events and depression, this research offers potential avenues for tailored therapeutic approaches and improved mental health outcomes. Neuroimaging studies that investigate the neural correlates of depression largely do not discern the difference between state-level depression and trait-level depression. State-level depression refers to short-term fluctuations in mood that are typically associated with changes in an individual’s environment or circumstance. Whilst trait level depression is considered to be stable over time and reflects long-term individual differences in depression level; being indexed by the personality trait of neuroticism. Such studies have used independent component analyses or seed-based correlation analyses to define networks. However, a newer technique, Probabilistic Functional Modes (PROFUMO) (Harrison et al., 2015) is a data decomposition method that adopts a hierarchical Bayesian model to optimize both group and subject estimates, simultaneously. PROFUMO was developed relatively recently and has not yet been tested in mental health population. The second aim of this thesis is to discern dissociable PROFUMO neural correlates of state versus trait depression using longitudinal and cross-sectional data. The findings revealed that functional connectivity was related to state depression, whereas spatial network organization was related to trait depression. In addition to association networks such as the default mode and salience network, state and trait depression were both associated with sensorimotor networks such as the visual and somatosensory networks. This study confirms the presence of differential neural correlates of state and trait depression and highlights the need to distinguish between the two in terms of informing biomarker usage for treatment tracking effects compared to identifying at-risk individuals. More so, the study highlights the need for further investigation into the role of sensorimotor networks in the onset of depression. Research into the neural correlates of life events largely focuses on individual classes of events, such that the cumulative neural correlates of an experience of multiple hassles/uplifts are unknown. The third aim of this thesis is to identify the neural correlates of cumulative life events using PROFUMO and to determine whether they overlap with the neural correlates of depression using a conjunction analysis. The findings revealed that functional connectivity and network organization in a similar circuitry of association and sensorimotor networks were associated with life events, but there were no directly overlapping neural correlates between depression and life events. This study highlights the complex interplay between life events and brain function and has the potential to guide the development of interventions that target specific neural networks, thereby leading to enhanced efficacy and improved mental health outcomes. Taken together, this thesis enhances our understanding of the cumulative impact of life events on depression and provides evidence for the differential PROFUMO neural correlates of state and trait depression. By identifying significant associations between specific life events, neural networks, and types of depression, this research provides valuable insights for the development of targeted interventions aimed at improving mental health outcomes.
Language
English (en)
Chair
Janine Bijsterbosch