Speaker
Description
We present a novel analytical framework to predict which behavioral domains are related to a pharmacological drug based on its mechanism of action. As proof-of-concept, we predict the behavioral profile of methylphenidate (MPH), a drug frequently prescribed to manage ADHD-related symptoms. For MPH's targets (dopamine transporter, noradrenaline transporter, and serotonin 1A receptor), we derived a cortical expression pattern using open-access transcriptomic AHBA data. Next, we spatially correlated this pattern with meta-analytic fMRI activation patterns that were predicted by NeuroQuery for behavioral Research Domain Criteria (RDoC) domains and constructs. The strongest correlations emerged within the Cognitive Systems domain including constructs Attention and Inhibition/Suppression. This result aligns with MPH's established efficacy in treating core ADHD symptoms. Additional correlations across the Negative Valence domain may be related to MPH's reported side effects anxiety and nervousness. Together, these findings indicate that behavioral effects of drugs may indeed be predicted based on their molecular targets.