Cognitive phenotyping has emerged as a powerful approach for characterizing individual variability in decision-making processes and affective states (Ging-Jehli et al., 2024). This method involves extracting computational parameters from behavioral tasks, providing various quantitative measures of reward and punishment processing. By applying computational models to participant behavior, researchers can obtain precise, mechanistic insights into the cognitive processes underlying complex psychiatric disorders such as depression and bipolar disorder (Robinson & Chase, 2017). These computational signatures can then be correlated with self-reported measures of anhedonia, depressive symptoms, perceived stress controllability, and affective states, offering a multi-dimensional perspective on the relationship between cognitive processes and clinical symptoms. This integrative approach promises to deepen our understanding of the cognitive and affective mechanisms underlying mood disorders, potentially leading to more targeted interventions and personalized treatment strategies (Adam et al., 2016). In addition to computational modeling, neuroimaging techniques such as MRI connectivity measures are being employed to further elucidate the neural mechanisms underlying mood disorders. Of particular interest is the role of hypothalamic dysregulation, which has been implicated in stress response and mood regulation (Marcolongo-Pereira et al., 2022; Jensen et al., 2024). By examining hypothalamic connectivity patterns, researchers can gain insights into how this crucial brain region interacts with other areas and whether dysregulation is associated with the symptoms severity. The integration of computational modeling, neuroimaging, and self-report measures represents a comprehensive approach to cognitive phenotyping in mood disorders. By combining these methodologies, researchers can create a more nuanced understanding of the interplay between cognitive processes, neural circuits, and clinical symptoms. This multi-modal approach not only enhances our ability to identify potential biomarkers but also contributes to the development of more targeted interventions and personalized treatment strategies for individuals suffering from mood disorders. The overreaching aim of the PhD studies is a deepening of understanding psychobiological factors and underlying affective dysregulation.