 this study investigated the differences between various mental workloads and different tasks. It found that certain nodes and connections in the brain exhibited distinct patterns of activity depending on the type of task being performed. These patterns could be used to accurately identify the level of mental workload in each task. Additionally, the study showed that the average classification accuracy of the SVM classifier was 95.8% for within task and 80.3% for cross-task mental workload discrimination. This suggests that it may be possible to use the dynamic functional connectivity metrics to accurately assess the level of mental workload across multiple tasks. This article was authored by Kai Guan, Ji Min Zhang, Xiao Ke Chai, and others.