The intrinsic functional connectivity patterns of the phonological and semantic networks in word reading

Previous studies have revealed that phonological and semantic processing recruit separate brain networks. However, the intrinsic functional connectivity patterns of the phonological and semantic networks remain unclear. To address this issue, the present study explored the static and dynamic functional connectivity patterns of phonological and semantic networks during the resting state. The static functional connectivity pattern of the two networks was examined by adopting a voxel-based global brain connectivity (GBC) method. In this analysis, we estimated the within-network connectivity (WNC), between-network connectivity between phonological and semantic networks (BNC_PS), and between-network connectivity of the two language networks (i.e., phonological and semantic networks) with the non-language network (BNC_N). The results showed that both phonological and semantic networks exhibited stronger intra-network connectivity (i.e., WNC) than inter-network connectivity (i.e., BNC_PS and BNC_N), indicating that both networks are relatively encapsulated. For dynamic functional connectivity, three distinct dynamic functional states were identified. Specifically, State 1 showed an overall positive connectivity pattern. State 2 exhibited an overall weak connectivity pattern. State 3 showed positive intra-network connectivity and negative inter-network connectivity. These results suggested that phonological and semantic networks exhibited a flexible integration and segregation pattern over time. Taken together, our results revealed that the phonological and semantic networks showed an intra-network integration and inter-network segregation pattern. These findings deepen our understanding of the intrinsic functional connectivity patterns of language networks.