Neuroaffective mechanisms of emotion regulation and dysregulation in healthy and clinical populations

What does it mean to be an emotion researcher? First of all, having no idea of what the object of study is. Indeed, there is still no general agreement about the definition of emotion, a vague concept that changes depending on the theoretical approach of each researcher. Given the important role they play in our lives influencing thoughts, behaviors, and social experiences, emotions have increasingly drawn the attention of several researchers in different domains. Specifically, the assumption that we are not slaves of our own emotions, but we can actively change them, has fostered a growing interest in emotion regulation. The field of affective neuroscience highlights the importance of integrating different methodological approaches (e.g., neuroimaging techniques, computational modeling, machine learning) to unveil the psychophysiological mechanisms and neural bases of emotional processes, providing insights about their impairments in mental disorders and the development of more accurate treatments. In light of this, in this thesis I will investigate the neural bases of emotion regulation, considering both its adaptive and detrimental aspects. The goal of the first part is to trace neurophysiological and brain structural representations of emotion regulation. In the second part, this construct will be explored by addressing its less adaptive counterparts, looking for morphometric evidence of emotion dysregulation. In the first study (Study I), I will investigate whether regulating emotions can leave a long-lasting trace in the brain, such as a neurophysiological ‘signature’ in the oscillatory activity, recording EEG signal at rest before and after applying an emotion regulation strategy. After exploring the physiological characterization of emotion regulation, the second study (Study II) will provide a morphometric representation of this process. A supervised machine-learning algorithm, namely Multi-Voxel Pattern Analysis (MVPA), will be applied on MRI images to identify structural networks predicting the use of specific cognitive strategies to regulate emotions. Studying mental disorders characterized by emotional difficulties can give us a direct window into neural mechanisms involved in emotion regulation. To address this issue, I will capitalize on Source-based Morphometry (SBM), a whole-brain multivariate approach to structural images based on Independent Component Analysis, a form of unsupervised machine learning to separate independent sources from a mixed-signal. In the third study (Study III), I will track down the neurostructural markers of emotion dysregulation focusing on Borderline Personality Disorder, whose core feature is dysfunctional emotion regulation, as compared to patients with Bipolar Disorder more characterized by mood disturbances and impulsive behavior. Along with emotions, the ability to control impulses can be dysregulated as well, representing a problematic symptom in many affective disorders. The fourth study (Study IV) will provide evidence of the neural bases of impulses dysregulation, investigating morphometric features of Bipolar Disorder. I will combine both subjective (self-report assessing impulsivity) and objective (MRI) measures, in order to gain a more comprehensive picture of this multifaceted dimension. These studies will be able to shed new light on emotion regulation processes, providing a wider overview of the underlying functional and dysfunctional mechanisms, thanks to the combination of neuroimaging techniques and subjective measures. According to a brain-behavioral approach, this will lead to build a model that can help to increase both scientific knowledge and everyday well-being.