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Parietal cortex engagement in direction selection during subway navigation

Poster No:

1887

Submission Type:

Abstract Submission

Authors:

Wenzhao Deng¹, Taihan Chen¹, Hongyimei Liu¹, Tiantian Liu¹, Zhongyin Liang¹, Zhizhong Jiang¹, Huihui Niu¹, Qing Qi², Ruiwang Huang¹

Institutions:

¹School of Psychology, Key Laboratory of Brain, South China Normal University, Guangzhou, Guangdong, ²School of Psychology, Trinity College Dublin, Dublin, Dublin

First Author:

Wenzhao Deng
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong

Co-Author(s):

Taihan Chen
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong

Hongyimei Liu
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong

Tiantian Liu
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong

Zhongyin Liang
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong

Zhizhong Jiang
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong

Huihui Niu
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong

Qing Qi
School of Psychology, Trinity College Dublin
Dublin, Dublin

Ruiwang Huang
School of Psychology, Key Laboratory of Brain, South China Normal University
Guangzhou, Guangdong

Introduction:

Spatial navigation refers to the ability of determining and maintaining a route from one spatial location to another (Parra-Barrero et al., 2023). Previous studies have highlighted the role of parietal cortex in spatial navigation, particularly in providing egocentric (i.e., self-centered) representations for navigation (Baumann & Mattingley, 2021; Cona & Scarpazza, 2019). Additionally, a few studies suggest that the parietal cortex encodes specific movement types (turn or go straight) during navigation (Nitz, 2006; Whitlock, 2017; Kira et al., 2023). However, these findings are largely based on research in rodents and monkeys, leaving it unclear whether the human parietal cortex similarly encodes movement types during navigation. Thus, we re-analyzed fMRI data from a navigation task (Liang et al., 2022; Qi et al., 2022) based on a 2D subway navigation paradigm adapted from Balaguer et al (2016). We aimed to investigate the different activation in the human parietal cortex associated with transitions between movement types.

Methods:

Subjects. We recruited 39 right-handed healthy adult subjects (24F/15M, 20.7 ± 2.2 years) from South China Normal University (SCNU) and nearby universities. The fMRI datasets from 6 subjects were excluded for their excessive head movement, and from 5 subjects were excluded because of their poor performance (overall accuracy rate < 80% in the navigation phases) in the fMRI experiment. The study was approved by the IRB of SCNU. Written informed consent was obtained from each subject.
Data acquisition and preprocessing. The fMRI data were acquired on a 3T Siemens Trio Tim MRI scanner with a 32-channel phased-array head coil and were preprocessed with SPM12. The preprocessing steps included: (1) field map correction, (2) head-motion correction, (3) normalization, (4) spatial smoothing, and (5) high-pass filtering at 1/128 Hz.
Experimental design. Figs. 1a and 1b illustrate the experimental procedure of the subway navigation task. We acquired the fMRI data from each subject when performing the task. The experiment included 4 runs of fMRI scan and each run consisted of 15–18 random journeys. Each journey included three phases, cue, navigation, and feedback. We recorded each subject's responses of switch and stay (Fig. 1c) during the navigation for subsequent analysis.
Whole-brain GLM analysis. We conducted a GLM analysis using SPM12 to detect brain regions with significant activation in the whole brain. The GLM analysis include 10 regressors: (R1) switch, (R2) stay, (R3) cue phase, (R4) feedback phase, and (R5-R10) six head movement parameters. We defined two contrasts of interest for the GLM, Switch > Stay (R1-R2) and Stay > Switch (R2-R1).

Results:

Fig. 1d shows the brain regions with significant activation in the bilateral postcentral gyrus (PCG), left superior parietal lobule (SPL), and left supramarginal gyrus (SMG) for Switch > Stay. However, no significant results were found for Stay > Switch. The detailed information for these clusters is listed in Table 1.

Conclusions:

We found that the PCG, SPL, and SMG were involved in processing movement type transitions (switch or stay) during the subway navigation task. These findings suggest that these brain regions were related to the control of direction during spatial navigation. The results are consistent with a previous study (Balaguer et al., 2016), indicating that humans may rely on the parietal cortex for path selection to achieve successful navigation.

Modeling and Analysis Methods:

Activation (eg. BOLD task-fMRI) ²

Novel Imaging Acquisition Methods:

BOLD fMRI ¹

Keywords:

FUNCTIONAL MRI

Other - Subway Navigation

^1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was this research conducted in the United States?

Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.

Yes, I have IRB or AUCC approval

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

SPM

Provide references using APA citation style.

Balaguer, J., Spiers, H., Hassabis, D., & Summerfield, C. (2016). Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network. Neuron, 90(4), 893–903. https://doi.org/10.1016/j.neuron.2016.03.037
Baumann, O., & Mattingley, J. B. (2021). Extrahippocampal contributions to spatial navigation in humans: A review of the neuroimaging evidence. Hippocampus, 31(7), 640–657. https://doi.org/10.1002/hipo.23313
Cona, G., & Scarpazza, C. (2019). Where is the "where" in the brain? A meta-analysis of neuroimaging studies on spatial cognition. Human Brain Mapping, 40(6), 1867–1886. https://doi.org/10.1002/hbm.24496
Kira, S., Safaai, H., Morcos, A. S., Panzeri, S., & Harvey, C. D. (2023). A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions. Nature Communications, 14(1), 2121. https://doi.org/10.1038/s41467-023-37804-2
Liang, Q., Li, J., Zheng, S., Liao, J., & Huang, R. (2022). Dynamic Causal Modelling of Hierarchical Planning. NeuroImage, 258, 119384. https://doi.org/10.1016/j.neuroimage.2022.119384
Nitz D. A. (2006). Tracking route progression in the posterior parietal cortex. Neuron, 49(5), 747–756. https://doi.org/10.1016/j.neuron.2006.01.037
Parra-Barrero, E., Vijayabaskaran, S., Seabrook, E., Wiskott, L., & Cheng, S. (2023). A map of spatial navigation for neuroscience. Neuroscience and Biobehavioral Reviews, 152, 105200. https://doi.org/10.1016/j.neubiorev.2023.105200
Qi, Q., Weng, Y., Zheng, S., Wang, S., Liu, S., Huang, Q., & Huang, R. (2022). Task-related connectivity of decision points during spatial navigation in a schematic map. Brain Structure & Function, 227(5), 1697–1710. https://doi.org/10.1007/s00429-022-02466-1
Whitlock J. R. (2017). Posterior parietal cortex. Current Biology : CB, 27(14), R691–R695. https://doi.org/10.1016/j.cub.2017.06.007

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