Spatiotemporal characterisation of information coding in the multiple demand network

The multiple-demand network (MDN), a set of highly interconnected, domain-general regions active across a wide variety of cognitively demanding tasks, is thought to support cognitive functions by integrating distinct types of information depending on the task. However, the spatiotemporal characteristics with which each node in the MDN encodes information remains unclear. We collected functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from separate participants performing a complex visual stimulus-response mapping task. We used multivariate pattern analysis (MVPA) to decode various task-related types of information-stimulus details, motor responses, and mapping rules-in both the MDN and visual areas. We used model-based MEG-fMRI fusion to compare the high temporal resolution data from MEG with high spatial resolution data from fMRI, extracting commonalities that reflect both the time course and location with which these different task features were represented. Early on, visual regions encoded information about the visual hemifield of the stimulus, while later, the MDN encoded the fine-grained details of the stimuli within the same hemifield and the task rules. We observed distinct temporal profiles of information coding for the cingulo-opercular versus frontoparietal sub-networks of the MDN. This study offers insights into the dynamic information processing of the MDN and provides information-coding-based support for at least two sub-networks within the multiple-demand network.