Spatiotemporal characterisation of information coding and exchange in the multiple demand network

The multiple-demand network (MDN), a brain-wide system with nodes near sensory and higher-order cognitive regions, has been suggested to integrate and exchange task-related information across the brain, supporting cognitive task performance. However, the profile of information coding and the role of each node within this network in information exchange remain unclear. To address this, we combined fMRI and MEG data in a challenging stimulus-response mapping task. Using multivariate pattern analysis (MVPA), we decoded various forms of task information, including coarse and fine stimulus details, motor responses, and stimulus-response mapping rules, across the MDN and visual regions. Early in the task, visual regions responded to large physical differences in stimuli, while later on, fine stimulus information and rules were encoded across the MDN. To assess information exchange between regions, we developed Fusion-RCA, a novel connectivity analysis method based on fMRI-MEG fusion profiles. Our findings revealed significant transfer of fine stimulus information, rules, and responses, but little evidence for the transfer of coarse stimulus information. These results highlight distinct information encoding patterns within MDN nodes and suggest that the anterior cingulate cortex (ACC) plays a key role in distributing task-relevant information. This study offers new insights into the dynamic function of the MDN and introduces Fusion-RCA as a powerful tool for exploring brain-wide information transfer.