Real-time fMRI neurofeedback and smartphone-based interventions to modulate mental functions

Our brains are constantly changing on a molecular level depending on the demands thrown at them by our environments, behavior, and thoughts. This neuronal plasticity allows us to voluntarily influence mental functions.
Taking conscious control over mental functions goes potentially back millenia, but it was psychotherapy since the early 20th century which moulded this concept into a concrete form to target specific mental disorders.
Mental disorders constitute a large burden on modern societies. Stress-related disorders like anxiety and depression particularly make up a large part of this burden and new ways to treat or prevent them are highly desirable, since traditional approaches are not equally helpful to every person affected.
This might be because the infrastructure is not available where the person lives, their schedules and obligations or financial means do not enable them to seek help or they simply do not respond to traditional forms of treatment.
Technological advances bring forth new potential approaches to modulate mental functions and allow using additional information to tailor an intervention better to an individual patient.
The focus of this dissertation lies on two promising approaches to cognitively intervene and modulate mental functions: real-time functional magnetic resonance imaging neurofeedback (rtfMRInf) on one hand and smartphone-based interventions (SBIs) on the other.
To investigate various aspects of both these methods in the context of stress and in relation to personalized interventions, we designed and conducted two experiments with a main rtfMRInf intervention, and also with ambulatory training of mental strategies, which participants accessed on their mobile phones.
The four publication this thesis entails, are related to this topic as follows:
The first publication focuses on rtfMRInf effects on the physiological stress response, exploring whether neurofeedback could reduce stress-related changes in brain activity and blood pressure.
The second publication focuses on rtfMRInf effects on psychological measures related to the stress response, namely on arousal and mood, based on data from self-report by the participants.
The third publication focuses on rtfMRInf methodology itself, looking at complex connectivity data between major neural networks.
Finally, the fourth publication focuses on personalized prediction of intervention success of an SBI using data from previous training days.