September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Dissociable dynamic network organization states for representations of relative and absolute spatial relations
Author Affiliations
  • Xin Hao
    State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
  • Zhencai Chen
    State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
  • Yiying Song
    State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
  • Xiangzhen Kong
    State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
  • Jia Liu
    Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, Beijing, China
Journal of Vision September 2018, Vol.18, 742. doi:10.1167/18.10.742
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Xin Hao, Zhencai Chen, Yiying Song, Xiangzhen Kong, Jia Liu; Dissociable dynamic network organization states for representations of relative and absolute spatial relations. Journal of Vision 2018;18(10):742. doi: 10.1167/18.10.742.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
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

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×