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2013 Aug 31. pii: S1053-8119(13)00381-9. doid: 10.1016/j.neuroimage.2013.04.034.

Differential and distributed effects of dopamine neuromodulations on resting-state network connectivity.

Cole DM, Beckmann CF, Oei NY, Both S, van Gerven JM, Rombouts SA

Dopaminergic medications, used to treat neurochemical pathology and resultant symptoms in neuropsychiatric disorders, are of mixed efficacy and regularly associated with behavioural side effects. The possibility that dopamine exerts both linear and nonlinear ('inverted U-shaped') effects on cognitive neurocircuitry may explain this outcome variability. However, it has proven to be difficult to characterise neural manifestations of psychopharmacological effects in humans. We hypothesised that diverse effects of dopamine neuromodulation could be characterised using systems-level neuroimaging approaches. Using 'resting-state' functional magnetic resonance imaging (FMRI), combined with dopaminergic challenges, we examined the dopamine-dependent functional connectivity of brain 'resting-state networks' (RSNs). We compared RSN connectivity in 3 groups of healthy volunteers given dopamine antagonist (haloperidol; N=18) or agonistic (levodopa; N=16) drugs, or a placebo (N=15). As RSNs have been shown to be relevant for numerous psychological functions and dysfunctions, we investigated both linear and nonlinear effects on RSN connectivity of manipulating dopamine neurotransmission pharmacologically. A basal ganglia RSN displayed both linear and nonlinear effects of dopamine manipulation on functional connectivity, respectively, with lateral frontoparietal and medial frontal neocortical areas. Conversely, a cognitive 'default mode' network showed only linear dopaminergic effects on connectivity with lateral frontal and parietal cortices. Our findings highlight diverse functional effects of dopamine neuromodulations on systems-level neural interactions. The observation that dopamine modulates distinct large-scale network connectivity patterns differentially, in both linear and nonlinear fashions, provides support for the objective utility of RSN metrics in classifying the effects and efficacy of psychopharmacological medications.