意思決定速度を予測する灰白質体積とギャンブル課題における大規模脳ネットワークの段階特異的寄与を明らかにする

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.