Resting-state dynamics as a neuromarker of dopamine administration in healthy female adults. (August 2019)

• Record Type: Journal Article
• Authors: Bellucci, Gabriele; Münte, Thomas F; Park, Soyoung Q
• Is Part Of: Journal of psychopharmacology. Volume 33:Number 8(2019)
• Page Start: 955
• Page End: 964
• Publication Date: 2019-08
• Subjects: Resting-state -- dopamine -- multivariate analysis -- functional connectivity -- facial attractiveness; Psychopharmacology -- Periodicals; 615.78
• DOI: 10.1177/0269881119855983
• Languages: English
• ISSNs: 0269-8811

Abstract

Background: Different neuromarkers of people's emotions, personality traits and behavioural performance have recently been identified. However, not much attention has been devoted to neuromarkers of neural responsiveness to drug administration. Aims: We investigated the predictive neuromarkers of acute dopamine (DA) administration. Methods: In a double-blind, within-subject study, we administrated a DA agonist (pramipexole) or placebo to 27 healthy female subjects. Using multivariate classification and prediction analyses, we examined whether dopaminergic modulations of task-free resting-state brain dynamics predict individual differences in pramipexole's modulation of facial attractiveness evaluations. Results: Our results demonstrate that pramipexole's effects on brain dynamics could be successfully discriminated from resting-state functional connectivity (accuracy: 78.9%; p < 0.0001). On the behavioural level, pramipexole increased facial attractiveness evaluations ( t (39) = 4.44; p < 0.0001). In particular, pramipexole administration enhanced connectivity strength of the cinguloopercular network ( t (23) = 3.29; p = 0.003) and increased brain signal variability in subcortical and prefrontal brain areas ( t (13) = 3.05, p = 0.009). Importantly, multivariate predictive models reveal that pramipexole-dependent modulation of resting-state dynamics predicted the increase of facial attractiveness evaluations after pramipexole (connectivity strength: standardized mean squared error, smse = 0.65; p = 0.0007; brain signal variability: smse = 0.94, p = 0.015). Conclusion: These results demonstrate that modulations of resting-state brain dynamics induced by a DA agonist predict drug-related effects on evaluation processes, providing a neuromarker of the neural responsiveness of specific brain networks to DA administration.