Abstract

The mental health conditions of anxiety and depression are relatively common conditions that are becoming defined as disorders of large-scale malfunctioning of brain networks rather than localised abnormalities. Brain connectivity network models provide a valuable framework for understanding how communication among distributed neural systems is influenced across various environments. Using graph-theoretical and network neuroscience analyses of functional and structural neuroimaging data, researchers have identified consistent connectivity alterations in core brain networks, including the default mode network (DMN), salience network (SN), and central executive network (CEN). These networks are characterised by abnormal integration and segmentation, i.e., hyperconnectivity of the DMN, reduction in connectivity in executive control areas and abnormal coupling of emotional and cognitive control systems in anxiety and depression. These abnormalities are associated with significant clinical factors, including excessive rumination, diminished emotion regulation, increased risk sensitivity, and cognitive inflexibility. Network-based measures also show variation in network hubs, efficiency, modularity, and the small-world phenomenon, which may indicate a transition to less optimal information processing in anxious and depressive states. Particularly, these connection patterns have standard and disorder-specific features that support transdiagnostic models of internalising psychopathology combined with brain markers that distinguish between anxiety and depression. Generally, brain connection models provide essential information on the neurobiological pathways underlying anxiety and depression. They can be used to understand how the diagnosis can be performed more effectively, to inform treatment stratification, and to develop tailored interventions based on the individual brain network.