Resilience, posttraumatic stress and recovery: insights from brain network dynamics

Abstract

In our lifetimes we will encounter varying degrees of stress and traumatic experiences, and how we respond to them will strongly affect our well-being and mental health. While there is considerable literature on the neural correlates of stress-related mental disorders, there are only very few models including a perspective on the neural factors that render us vulnerable to stress and how therapeutic interventions can modulate brain activity to help us recover. This is in part due to the scarcity of longitudinal studies necessary to investigate both the trajectories of maladaptive changes that make us vulnerable as well as the positive changes that allow us to rebalance our brain back to healthy functioning. Moreover, only very few human neuroimaging studies have been able to incorporate a perspective on the temporal dimension of brain activation patterns. This is specially important given the highly dynamic nature of the stress response, and the current perspectives that propose fast shifts in large-scale brain networks as key in the production of an adaptive response to stress. In this thesis, we started by investigating brain function in relation to stress and stress vulnerability using a standard method of static functional connectivity on a longitudinal dataset of healthy soldiers exposed to stress (Chapter 2). This allowed us to define a starting point comparable to most of the available fMRI studies on stress and revealed a broad network of differences between stress conditions. We then used a recently developed method (i.e., Leading Eigenvector Dynamic Analysis) to investigate the stability and temporal dominance of discrete and recurrent functional connectivity patterns (or states) showing that increased temporal stability of a frontoparietal functional connectivity state was associated with stress vulnerability (Chapter 3). Exploration of transitions between the detected functional connectivity states showed that the increase in temporal stability was furthermore accompanied by decreased transitions from the frontoparietal to the default mode state in vulnerable participants (Chapter 4). We then applied a second state-of-the-art method (i.e., Hidden Markov Models) to detect recurrent activation patterns in a dataset of participants with PTSD before and after cognitive therapy. We showed that PTSD is associated with a decrease in the temporal dominance of activation patterns related to the default mode network. Moreover, cognitive therapy was related to normalization of these default mode activation patterns back to a level similar to that of healthy participants (Chapter 5). In the final chapter, we integrate our findings into a brain dynamic perspective of stress vulnerability, PTSD and recovery.</p