Resting-state functional neuroimaging biomarkers in depression: toward a dimensional approach for personalized diagnosis and treatment

Major depressive disorder (MDD) is traditionally diagnosed using symptom criteria systems, which allow for up to 227 distinct symptom combinations to meet the same diagnostic criteria. This nosological framework contributes to substantial clinical heterogeneity and has limited the efforts to identify reliable neurobiological biomarkers. However, recent advancements in resting-state functional connectivity (rsFC) and the emergence of dimensional models of psychopathology offer promising avenues for more precisely characterize the neural substrates underlying specific clinical features of depression. This narrative review highlights consistent rsFC findings related to the most extensively studied clinical dimensions: anhedonia, rumination, insomnia, and suicide. Consummatory anhedonia is consistently associated with lower connectivity between the ventral striatum (VS) and the prefrontal cortex (PFC), while anticipatory anhedonia involves rsFC alterations between the VS and multiple brain regions. Brooding—the maladaptive component of rumination— shows higher connectivity within nodes of the default mode network (DMN), while reflective rumination recruits executive and salience networks. Insomnia is associated with disrupted connectivity within central hubs of the DMN. Suicide reveals both shared and distinct rsFC abnormalities across ideation and attempts, especially involving the orbitofrontal cortex, anterior cingulate cortex, and insula. These findings support the existence of dimension-specific neurofunctional profiles in depression and highlight the limitations of the classic diagnostic definition. We advocate for a paradigm shift toward brain-based dimensional frameworks, consistent with the National Institute of Mental Health’s Research Domain Criteria (RDoC) principles, to advance personalized and targeted interventions.