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From rest to focus: Pharmacological modulation of the relationship between resting state dynamics and task-based brain activation

Kathryn BiernackiiD, Tianye ZhaiiD, Justine HilliD, Emily McConnell, Betty Jo SalmeroniD, Roselinde H. KaiseriD, Amy C. JanesiD

Abstract

Dynamic resting-state brain activity provides insight into intrinsic neural function and holds promise for predicting individual responses to cognitive demands and pharmacological interventions. This research could ultimately guide medication selection, yet links between network dynamics and medication effects on cognitive function require further validation. Here, we examined whether dynamic activity of an attentional network at rest relates to task-evoked brain activation on the Multi Source Interference Task (MSIT) following administration of methylphenidate (20mg) and haloperidol (2mg), which have opposing effects on attention and catecholaminergic function. Fifty-nine healthy adults completed resting-state and task-based fMRI on three separate days on which they received methylphenidate, haloperidol, or placebo in a double-blind placebo-controlled design. Coactivation pattern analysis determined time spent in the dorsal attention network (DAN) under placebo at rest. Linear mixed-effects modelling assessing the relationship between MSIT task activation under drug and time spent in DAN at rest under placebo and MSIT task activation under drug identified a significant interaction in the dorsolateral prefrontal cortex (dlPFC; p<0.001). Post-hoc analyses indicated that more time in the DAN at rest under placebo was associated with decreased MSIT dlPFC activation under methylphenidate and increased dlPFC activation under haloperidol. Findings demonstrate that resting dynamics of an attentional network are linked to task-related brain responses under different drug conditions within a region implicated in attentional control and sensitive to catecholaminergic variance. Resting-state dynamics may predict pharmacological modulation of goal-directed cognition, highlighting the potential clinical utility of resting-state dynamics in predicting medication response and supporting individualized treatment.

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