The stress connection: Neuroimaging studies of emotion circuits in social stress, personality, and stress-related psychopathology

The aim of this thesis was to identify the neural mechanisms that enable a person to adaptively respond to, and recover from stress, which was studied in healthy controls, in people with increased vulnerability or resilience to stress-related disorders, and in people with depression or PTSD, using magnetic resonance imaging (MRI). In most of the studies, a specific MRI method was employed, with which it is possible to assess how different brain regions communicate with each other (i.e., functional connectivity) when the brain is initiating or regulating stress responses. Structure, activity, and connectivity of the amygdala, a small brain region important for stress reactivity, was of main interest. The results show how stress influences information processing, and causes changes in the communication between brain areas, even long after the stressful event ended. Furthermore, personality dimensions associated with increased vulnerability or resilience to affective disorders were associated with changes in brain networks involved in emotion processing and regulation. Finally, smaller amygdala volumes were found in women with PTSD, while reduced integrity of affective brain networks was demonstrated in depression. Together, these results open important new avenues for future research into the short and long term effects of stress on the brain.Multivariate analysis of psychological dat

Stress and streets: How the network structure of streets is associated with stress-related brain activation

Previous research has examined the relation between urban design factors and mental health, but the impact of street networks is underrepresented. This exploratory, cross-sectional study examines the association between street network variables based on the Space Syntax theory and whole-brain activation during a social stress paradigm. Forty-two individuals who lived in Berlin participated in an fMRI study during which acute social stress was induced. Saliva cortisol concentrations, subjective stress ratings, and mean heart rate were assessed as proxies for a successful stress induction. Space Syntax was used as a tool to objectively measure street network characteristics including global integration (‘proximity’ of a street to all the other streets in a network), local integration (‘proximity’ of a street to a certain number of streets within a walkable area), connectivity (‘direct street connections’ a street has), and normalized angular choice (NACH) (‘straightest and shortest’ route for a street in a street network). They were analyzed within a 1500 m radius of participants' address (i.e., neighborhood) as well as for the street closest to their address (i.e., point address). Higher mean neighborhood global and local integration, which equate to better integrated streets in the network, were associated with less activation during stress provocation in several brain regions, including dorsal anterior cingulate cortex, insula, and thalamus, which play a role in the detection of salient stimuli and threats. No association was found between brain activity and global and local integration for the point address. There was also no association between brain activity and connectivity or NACH for any conditions. The study indicates that Space Syntax is a useful tool for measuring macro-scale urban space (e.g., street networks) in neuro-urbanistic studies. The results underline the need to explore the potential of optimizing street networks to better understand pathways to urban mental health.</p

A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility

Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks.</p