Catecholaminergic modulation of large-scale network dynamics is tied to the reconfiguration of corticostriatal connectivity

Large-scale brain network function is critical for healthy cognition, yet links between such network function, neurochemistry, and smaller-scale neurocircuitry are unclear. Here, we evaluated 59 healthy individuals using resting-state fMRI to determine how network-level temporal dynamics were impacted by two well-characterized pharmacotherapies targeting catecholamines: methylphenidate (20mg) and haloperidol (2mg). Network dynamic changes were tested for links with drug-induced alterations in complex corticostriatal connections as this circuit is a primary site of action for both drugs. A randomized, double-blind, placebo-controlled design was used. Methylphenidate enhanced time spent in the default mode network (DMN p<0 .001) and dorsal attention network (DAN p<0.001) and reduced time in the frontoparietal network (p<0.01). Haloperidol increased time in a sensory motor-DMN state (p<0.01). The magnitude of change in network dynamics induced by methylphenidate vs. placebo was correlated with the magnitude of methylphenidate-induced rearrangement of complex corticostriatal connectivity (R=0.32, p=0.014). Haloperidol did not alter complex corticostriatal connectivity. Methylphenidate increased time in networks involved in internal (DMN) and external attention (DAN), aligning with methylphenidates established role in attention. Methylphenidate also significantly changed complex corticostriatal connectivity by altering the relative strength between multiple corticostriatal connections, indicating that methylphenidate may shift which corticostriatal connections are prioritized relative to others. Findings further show that these local circuit changes are linked with large scale network function. Collectively, these findings provide a deeper understanding of large-scale network function, set a stage for mechanistic understanding of network engagement, and provide needed information to potentially guide medication use based on network-level effects.