Affective dysregulation, dissociation, and complex neural networks: Two exploratory mediation models

Affective dysregulation and dissociation are central transdiagnostic processes that contribute to the onset and maintenance of complex psychopathology. Drawing from both clinical and neuroscientific frameworks, this study tested two exploratory mediation models to investigate how psychological distress and well-being interact with dissociative symptoms and dysfunctions in large-scale neural networks. A sample of adults (N = 179) completed validated self-report measures assessing affective states, dissociation, and neural network dysfunction. In Model 1, dissociation emerged as a significant mediator of the relationship between distress and neural network dysfunction, particularly through depersonalization/derealization. Well-being exerted a direct protective effect on neural function, while distress exerted an indirect detrimental effect. In Model 2, neural networks, particularly the Default Mode Network (DMN) and Amygdaloid–Hippocampal Memory Network (AHMN), mediated the effects of affective states on dissociation. Distress predicted increased DMN dysfunction, which in turn predicted higher dissociation; well-being showed the opposite pattern. These results underscore the bidirectional interplay between affective dysregulation, dissociation, and brain network dysfunction. Clinically, the findings support targeting dissociation and fostering psychological well-being as strategies to enhance neural integration.