Abstract
Identifying and extracting spatial relations is a fundamental aspect in spatial navigation. The representations of two main types of spatial relation, relative and absolute spatial relations, have been investigated in numerous studies, gaining some insights into the different brain regions involved. However, no study has investigated the neural basis for representations of relative and absolute spatial relations from a dynamic network view. Here, we used a dynamic functional connectivity (FC) approach to explore how representations of relative and absolute spatial relations are associated with different dynamic FC states of the navigation network in a large cohort of participants (N = 226). After identifying the navigation network, we separated it into a core and an extended network with a modularity analysis. We clustered all time windows during resting-state scanning into two typical states for dynamic FC within the core network (or dWNC), a weak state (Mean FC = 0.31) and a strong state (Mean FC = 0.68). Meanwhile, two typical states for dynamic FC between core and extended network (or dBNC) were identified, a negative state (Mean FC = -0.26) and a positive state (Mean FC = 0.19). Interestingly, we found that topographic scene recognition based on relative spatial relations was only related to properties of the weak state of dWCN, but not to any states of dBNC. In contrast, sense of distance based on absolute spatial relations was associated with properties of the negative state of dBNC, but not with any states of dWCN. These results indicated representation of relative spatial relations was related to integration within the core navigation network, while representation of absolute spatial relations was related to interactions between the core and extended navigation network. In sum, our study reveals double dissociation in dynamic network organization states underlying the representations of relative and absolute spatial relations.
Meeting abstract presented at VSS 2018