Neural Systems Underlying the Implementation of Working Memory Removal Operations.

Recently, multi-voxel pattern analysis has verified that information can be removed from working memory (WM) via three distinct operations , , or compared to information being ( Kim et al., 2020) While univariate analyses and classifier importance maps in Kim et al. (2020) identified brain regions that contribute to these operations, they did not elucidate whether these regions represent the operations similarly or uniquely. Using Leiden-community-detection on a sample of 55 humans (17 male), we identified four brain networks, each of which has a unique configuration of multi-voxel activity patterns by which it represents these WM operations. The visual network (VN) shows similar multi-voxel patterns for and , which are highly dissimilar from and , suggesting this network differentiates whether an item is held in WM or not. The somatomotor network (SMN) shows a distinct multi-voxel pattern for relative to the other operations, indicating the uniqueness of this operation. The default mode network (DMN) has distinct patterns for and , but these two operations are more similar to each other than to and , a pattern intermediate to that of the VN and SMN. The frontoparietal control network (FPCN) displays distinct multi-voxel patterns for each of the four operations, suggesting that this network likely plays an important role in implementing these WM operations. These results indicate that the operations involved in removing information from WM can be performed in parallel by distinct brain networks, each of which has a particular configuration by which they represent these operations.