Predicting individual variations in mental effort-based decision-making using machine learning: Neurometabolic signature in the dorsomedial prefrontal cortex/dorsal anterior cingulate cortex

Abstract

AbstractExploring why individuals vary in their willingness to exert effort in decision-making is fundamental for understanding human behavior. Our study focuses on the dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC), a crucial brain region in motivation and decision-making, to uncover the neurobiological factors influencing these individual differences. We utilized 7T proton magnetic resonance spectroscopy (1H-MRS) to analyze metabolite concentrations in the dmPFC/dACC and anterior insula (AI) of 75 participants, aiming to predict individual variability in effort-based decision-making. Employing computational modeling, we identified key motivational parameters and, using machine learning models, pinpointed glutamate, aspartate, and lactate as crucial metabolites predicting decision-making to exert high mental effort, signifying their role as potential biomarkers for mental effort decision-making. Additionally, we examined the relationships between plasma and brain metabolite concentrations. Our findings provide novel insights into the neurometabolic underpinnings of motivated behavior, offering new perspectives in the field of cognitive neuroscience and human behavior.