Gray matter volume predicts decision speed and reveals stage-specific contributions of large-scale brain networks in gambling tasks

Large-scale brain networks are well-established in resting-state research and are increasingly being used in task-based functional magnetic resonance imaging (fMRI) studies. However, the mechanisms by which brain networks dynamically reorganize across the various stages of decision-making remain unclear. Here, we investigated the neural basis of decision-making by integrating voxel-based morphometry and fMRI within a modified "Wheel of Fortune" gambling task. Stage-specific brain activation was characterized using the Yeo-7 network atlas to delineate large-scale network dynamics across task stages. We found that: (1) Reaction time (RTs) were significantly longer during choose conditions compared to follow conditions; (2) Gray matter volume correlated with individual variability in RT and predicted RT during choose conditions using multivariate pattern analysis with a Kernel Ridge Regression model, effects absent during follow conditions; (3) A negative correlation was observed between RT and activation in the right superior temporal gyrus and left mid-cingulate cortex; (4) Choice stage involved more extensive network engagement than the result and rating stages, with the rating stage showing the lowest overall activation. Network-specific fractional contributions revealed dominant engagement of the ventral attention network, default mode network, and somato-motor network during the choice stage; the frontoparietal network (FPN), dorsal attention network (DAN), and visual network during the result stage; and the DAN and FPN during the rating stage. These findings provide structural and functional explanations for individual differences in decision speed within a gambling paradigm, revealing the distinct and dynamic roles of brain networks across decision stages and offering mechanistic insights into the neural architecture of this process.