Abstract
Decades of preclinical research have implicated divergent roles of basal and task-based activity within the mesolimbic and mesocortical dopamine pathways in the neurobiological mechanisms of depression. However, translation to humans has been limited by low-resolution neuroimaging methods during the inherent computational nature of learning processes. Here, utilizing high-resolution, precision multi-echo functional MRI, Bayesian modeling, and graph-theory network influence modeling, we demonstrate that relative to controls, individuals with major depressive disorder (MDD) are characterized by an elevated influence of the ventral tegmental area (VTA) within the mesolimbic/mesocortical network during task-free brain states. During task-based brain states, mesocortical hyperconnectivity was linked to blunted neural tracking of reward prediction error (RPE) signals in the nucleus accumbens and greater anhedonia, while blunted neural tracking of RPEs in the basolateral amygdala was linked with greater anxiety. Thus, mesocortical hyperconnectivity in MDD may lead to a computationally underutilized learning mechanism that impacts discrete depression and anxiety symptom domains. Together these results may explain paradoxical preclinical work and elucidate neural underpinnings for the clinical heterogeneity of depression.