improving resolution of dynamic communities in human brain networks through targeted node removal current approaches to dynamic community detection in complex networks can fail to identify multiscale community structure or to resolve key features of community dynamics we propose a targeted node removal technique to improve the resolution of community detection using synthetic oscillator networks with welldefined ground truth communities we quantify the community detection performance of a common modularity maximization algorithm we show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multiscale networks with communities of different sizes compared to the performance in a singlescale network we demonstrate that targeted node removal during community detection improves performance on multiscale networks particularly when removing the most functionally cohesive nodes applying this approach to network neuroscience we compare dynamic functional brain networks derived from fmri data taken during both repetitive singletask and varied multitask experiments after the removal of regions in visual cortex the most coherent functional brain area during the tasks community detection is better able to resolve known functional brain systems into communities in addition node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments revealing taskswitching behavior that was not identified with the visual regions present in the network these results indicate that targeted node removal can improve spatial and temporal resolution in community detection and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters