Spatial Navigation Ability is Associated with Topological Features of the Human Navigation Network
Poster No:
862
Submission Type:
Late-Breaking Abstract Submission
Authors:
Marla Pinkert1, Lars Keuter2, Isabella Wagner1,3
Institutions:
1Faculty of Psychology, University of Vienna, Vienna, Austria, 2Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 3Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
First Author:
Co-Author(s):
Lars Keuter
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf
Hamburg, Germany
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf
Hamburg, Germany
Isabella Wagner
Faculty of Psychology, University of Vienna|Vienna Cognitive Science Hub, University of Vienna
Vienna, Austria|Vienna, Austria
Faculty of Psychology, University of Vienna|Vienna Cognitive Science Hub, University of Vienna
Vienna, Austria|Vienna, Austria
Introduction:
Spatial navigation involves a set of brain regions such as the hippocampus and adjacent medial temporal lobe structures, the posterior medial cortex, and the anterior thalamus (Ekstrom et al., 2017). Previous research showed that resting-state functional connectivity (FC) between the "human navigation network" was associated with individual differences in self-reported navigation ability (Kong et al., 2017). However, whether functional connectivity is linked to navigation ability in more naturalistic task setups is unclear. Here, we used graph theory to quantify specific topological features of the human navigation network at rest, including modularity, small-worldness, and betweenness centrality of "hub" regions, and asked whether they were tied to navigation ability in virtual reality.
Methods:
Seventy-nine participants volunteered for this study, taking part across two days a week apart (Fig 1A). On day 1, participants were trained in a navigation task in which they had to learn the location of different objects in a virtual reality environment (Fig 1B). On day 2, they completed a resting-state fMRI period, followed by two runs of the navigation task. This allowed us to assess the initial navigation improvement on day 1, as well as the stable navigation performance on day 2. Participants also self-reported their navigation ability using the Santa Barbara Sense of Direction Scale (Fig 1 C; Hegarty et al., 2002). The resting-state fMRI data were preprocessed using the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL; Jenkinson et al., 2012), Advanced Normalization Tools (ANTS; Avants et al., 2008), and ICA-AROMA (Pruim et al., 2015). We defined 16 network nodes that were previously associated with spatial navigation (Fig 1D; Ekstrom et al., 2017; Li et al., 2021). The signal timecourse of each node was extracted to calculate partial correlations between nodes. The resulting functional connectivity matrices were thresholded, keeping 20% of the strongest connections. Thus, we obtained an unweighted adjacency matrix for each participant (Fig 1E). These adjacency matrices were averaged to calculate a mean navigation network (Fig 1F). We used NetworkX (Hagberg et al., 2008) to quantify modularity and small-worldness of the navigation network, and the betweenness centrality of each node.
Results:
The mean navigation network (Fig 1F) was characterized by a relatively modular structure, yielding a value of 0.44 (values between 0.3-0.7 appear typical for networks with strong modular structure; Newman & Girvan, 2004). Based on their betweenness centrality (>1 standard deviation above the mean), we identified four "hubs": the right posterior cingulate cortex (PCC), bilateral retrosplenial cortex (RSC), left posterior medial temporal lobe (pMTL), and the left anterior thalamus.
Using multiple regression, we assessed the associations between initial and stable navigation ability (day 1 and 2, respectively) with modularity, small-worldness, and betweenness centrality of the four hub regions. We found that initial navigation ability was negatively associated with betweenness centrality of the left RSC (ß = -33.85, p = .005, Fig 1GF) and the right anterior thalamus (ß = -33.16, p = .038, Fig 1H). In other words, participants who learned to navigate the virtual reality environment quicker showed decreased "hubness" of the left RSC and anterior thalamus.
Using multiple regression, we assessed the associations between initial and stable navigation ability (day 1 and 2, respectively) with modularity, small-worldness, and betweenness centrality of the four hub regions. We found that initial navigation ability was negatively associated with betweenness centrality of the left RSC (ß = -33.85, p = .005, Fig 1GF) and the right anterior thalamus (ß = -33.16, p = .038, Fig 1H). In other words, participants who learned to navigate the virtual reality environment quicker showed decreased "hubness" of the left RSC and anterior thalamus.
Conclusions:
Overall, we found that navigation improvement in a virtual reality-based navigation task was negatively associated with betweenness centrality of the left RSC and the right anterior thalamus. We suggest that hyperconnectivity of these hubs within the navigation network may negatively impact navigation learning.
Learning and Memory:
Skill Learning 1
Learning and Memory Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis
Keywords:
Cognition
FUNCTIONAL MRI
Memory
Other - Spatial Navigation; Graph Theory
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Ekstrom, A. D. (2017). Interacting networks of brain regions underlie human spatial navigation: A review and novel synthesis of the literature. Journal of Neurophysiology, 118(6), 3328–3344. https://doi.org/10.1152/jn.00531.2017
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Jenkinson, M. (2012). FSL. NeuroImage, 62(2), 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Kong, X.-Z. (2017). Human navigation network: The intrinsic functional organization and behavioral relevance. Brain Structure and Function, 222(2), 749–764. https://doi.org/10.1007/s00429-016-1243-8
Li, J. ( 2021). Human spatial navigation: Neural representations of spatial scales and reference frames obtained from an ALE meta-analysis. NeuroImage, 238, 118264. https://doi.org/10.1016/j.neuroimage.2021.118264
Newman, M. E. J. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. https://doi.org/10.1103/PhysRevE.69.026113
Pruim, R. H. R. (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage, 112, 267–277. https://doi.org/10.1016/j.neuroimage.2015.02.064