Topology properties of spatial navigation-related functional brain networks in crowds: a study based on graph theory analysis_West China Medical Journal

Authors：

CHANG Linli ^1,2 , ZHANG Yanhai ³ ,  CHI Liyi ² , WANG Huihui ⁴ , BAI Wanqi ² , ZHANG Linjing ⁵ , XU Zhaoxia ⁶

1. The Second Clinical Medical College, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi 712000, P. R. China;
2. Department of Neurology, Air Force 986 Hospital, Air Force Medical University, Xi’an, Shaanxi 710000, P. R. China;
3. The First Outpatient Department, Air Force 986 Hospital, Air Force Medical University, Xi’an, Shaanxi 710000, P. R. China;
4. Department of Neurology, Xianyang Hospital of Yan’an University, Xianyang, Shaanxi 712000, P. R. China;
5. Department of Pharmacy, Air Force 986 Hospital, Air Force Medical University, Xi’an, Shaanxi 710000, P. R. China;
6. Department of Imaging, Air Force 986 Hospital, Air Force Medical University, Xi’an, Shaanxi 710000, P. R. China;

Corresponding author：

CHI Liyi, Email: 1466101081@qq.com

Keywords：

Spatial navigation; functional brain networks; graph theory analysis; resting-state functional magnetic resonance imaging; topology; small-world network properties

DOI：

10.7507/1002-0179.202302033

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Objective To investigate the differences in the topology of functional brain networks between populations with good spatial navigation ability and those with poor spatial navigation ability. Methods From September 2020 to September 2021, 100 college students from PLA Army Border and Coastal Defense Academy were selected to test the spatial navigation ability. The 25 students with the highest spatial navigation ability were selected as the GN group, and the 25 with the lowest spatial navigation ability were selected as the PN group, and their resting-state functional MRI and 3D T1-weighted structural image data of the brain were collected. Graph theory analysis was applied to study the topology of the brain network, including global and local topological properties. Results The variations in the clustering coefficient, characteristic path length, and local efficiency between the GN and PN groups were not statistically significant within the threshold range (P>0.05). The brain functional connectivity networks of the GN and PN groups met the standardized clustering coefficient (γ)>1, the standardized characteristic path length (λ)≈1, and the small-world property (σ)>1, being consistent with small-world network property. The areas under curve (AUCs) for global efficiency (0.22±0.01 vs. 0.21±0.01), γ value (0.97±0.18 vs. 0.81±0.18) and σ value (0.75±0.13 vs. 0.64±0.13) of the GN group were higher than those of the PN group, and the differences were statistically significant (P<0.05); the between-group difference in AUC for λ value was not statistically significant (P>0.05). The results of the nodal level analysis showed that the AUCs for nodal clustering coefficients in the left superior frontal gyrus of orbital region (0.29±0.05 vs. 0.23±0.07), the right rectus gyrus (0.29±0.05 vs. 0.23±0.09), the middle left cingulate gyrus and its lateral surround (0.22±0.02 vs. 0.25±0.02), the left inferior occipital gyrus (0.32±0.05 vs. 0.35±0.05), the right cerebellar area 3 (0.24±0.04 vs. 0.26±0.03), and the right cerebellar area 9 (0.22±0.09 vs. 0.13±0.13) were statistically different between the two groups (P<0.05). The differences in AUCs for degree centrality and nodal efficiency between the two groups were not statistically significant (P>0.05). Conclusions Compared with people with good spatial navigation ability, the topological properties of the brains of the ones with poor spatial navigation ability still conformed to the small-world network properties, but the connectivity between brain regions reduces compared with the good spatial navigation ability group, with a tendency to convert to random networks and a reduced or increased nodal clustering coefficient in some brain regions. Differences in functional brain network connectivity exist among people with different spatial navigation abilities.

Citation： CHANG Linli, ZHANG Yanhai, CHI Liyi, WANG Huihui, BAI Wanqi, ZHANG Linjing, XU Zhaoxia. Topology properties of spatial navigation-related functional brain networks in crowds: a study based on graph theory analysis. West China Medical Journal, 2023, 38(8): 1160-1166. doi: 10.7507/1002-0179.202302033 Copy

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17.	Miller KJ, Botvinick MM, Brody CD. Value representations in the rodent orbitofrontal cortex drive learning, not choice. Elife, 2022, 11: e64575.
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19.	Sato N, Sakata H, Tanaka YL, et al. Navigation-associated medial parietal neurons in monkeys. Proc Natl Acad Sci U S A, 2006, 103(45): 17001-17006.
20.	Schindler A, Bartels A. Integration of visual and non-visual self-motion cues during voluntary head movements in the human brain. Neuroimage, 2018, 172: 597-607.
21.	Liu B, Tian Q, Gu Y. Robust vestibular self-motion signals in macaque posterior cingulate region. Elife, 2021, 10: e64569.
22.	Grill-Spector K. The neural basis of object perception. Curr Opin Neurobiol, 2003, 13(2): 159-166.
23.	Dilks DD, Julian JB, Paunov AM, et al. The occipital place area is causally and selectively involved in scene perception. J Neurosci, 2013, 33(4): 1331-1336.
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25.	Suzuki S, Kamps FS, Dilks DD, et al. Two scene navigation systems dissociated by deliberate versus automatic processing. Cortex, 2021, 140: 199-209.
26.	Epstein RA, Baker CI. Scene perception in the human brain. Annu Rev Vis Sci, 2019, 5: 373-397.
27.	Qiu Y, Wu Y, Liu R, et al. Representation of human spatial navigation responding to input spatial information and output navigational strategies: an ALE meta-analysis. Neurosci Biobehav Rev, 2019, 103: 60-72.
28.	Choe KY, Sanchez CF, Harris NG, et al. Optogenetic fMRI and electrophysiological identification of region-specific connectivity between the cerebellar cortex and forebrain. Neuroimage, 2018, 173: 370-383.
29.	Watson TC, Obiang P, Torres-Herraez A, et al. Anatomical and physiological foundations of cerebello-hippocampal interaction. Elife, 2019, 17(8): e41896.

1. Park HJ, Friston K. Structural and functional brain networks: from connections to cognition. Science, 2013, 342(6158): 1238411.
2. Raichle ME. Two views of brain function. Trends Cogn Sci, 2010, 14(4): 180-190.
3. Ekstrom AD, Huffman DJ, Starrett M. Interacting networks of brain regions underlie human spatial navigation: a review and novel synthesis of the literature. J Neurophysiol, 2017, 118(6): 3328-3344.
4. Julian JB, Keinath AT, Marchette SA, et al. The neurocognitive basis of spatial reorientation. Curr Biol, 2018, 28(17): R1059-R1073.
5. Lester AW, Moffat SD, Wiener JM, et al. The aging navigational system. Neuron, 2017, 95(5): 1019-1035.
6. Ramanoël S, York E, Le Petit M, et al. Age-related differences in functional and structural connectivity in the spatial navigation brain network. Front Neural Circuits, 2019, 13: 69.
7. Li AWY, King J. Spatial memory and navigation in ageing: a systematic review of MRI and fMRI studies in healthy participants. Neurosci Biobehav Rev, 2019, 103: 33-49.
8. Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nature, 1998, 393(6684): 440-442.
9. Sporns O. Network attributes for segregation and integration in the human brain. Curr Opin Neurobiol, 2013, 23(2): 162-171.
10. Hallquist MN, Hillary FG. Graph theory approaches to functional network organization in brain disorders: a critique for a brave new small-world. Netw Neurosci, 2018, 3(1): 1-26.
11. Bassett DS, Bullmore ET. Small-world brain networks revisited. Neuroscientist, 2017, 23(5): 499-516.
12. Li W, Zhao H, Qing Z, et al. Disrupted network topology contributed to spatial navigation impairment in patients with mild cognitive impairment. Front Aging Neurosci, 2021, 13: 630677.
13. Bai F, Shu N, Yuan Y, et al. Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. J Neurosci, 2012, 32(12): 4307-4318.
14. Wikenheiser AM, Schoenbaum G. Over the river, through the woods: cognitive maps in the hippocampus and orbitofrontal cortex. Nat Rev Neurosci, 2016, 17(8): 513-523.
15. Patai EZ, Spiers HJ. The Versatile Wayfinder: prefrontal contributions to spatial navigation. Trends Cogn Sci, 2021, 25(6): 520-533.
16. Elliott Wimmer G, Büchel C. Learning of distant state predictions by the orbitofrontal cortex in humans. Nat Commun, 2019, 10(1): 2554.
17. Miller KJ, Botvinick MM, Brody CD. Value representations in the rodent orbitofrontal cortex drive learning, not choice. Elife, 2022, 11: e64575.
18. Gu Y, Cheng Z, Yang L, et al. Multisensory convergence of visual and vestibular heading cues in the pursuit area of the frontal eye field. Cereb Cortex, 2016, 26(9): 3785-3801.
19. Sato N, Sakata H, Tanaka YL, et al. Navigation-associated medial parietal neurons in monkeys. Proc Natl Acad Sci U S A, 2006, 103(45): 17001-17006.
20. Schindler A, Bartels A. Integration of visual and non-visual self-motion cues during voluntary head movements in the human brain. Neuroimage, 2018, 172: 597-607.
21. Liu B, Tian Q, Gu Y. Robust vestibular self-motion signals in macaque posterior cingulate region. Elife, 2021, 10: e64569.
22. Grill-Spector K. The neural basis of object perception. Curr Opin Neurobiol, 2003, 13(2): 159-166.
23. Dilks DD, Julian JB, Paunov AM, et al. The occipital place area is causally and selectively involved in scene perception. J Neurosci, 2013, 33(4): 1331-1336.
24. Julian JB, Ryan J, Hamilton RH, et al. The occipital place area is causally involved in representing environmental boundaries during navigation. Curr Biol, 2016, 26(8): 1104-1109.
25. Suzuki S, Kamps FS, Dilks DD, et al. Two scene navigation systems dissociated by deliberate versus automatic processing. Cortex, 2021, 140: 199-209.
26. Epstein RA, Baker CI. Scene perception in the human brain. Annu Rev Vis Sci, 2019, 5: 373-397.
27. Qiu Y, Wu Y, Liu R, et al. Representation of human spatial navigation responding to input spatial information and output navigational strategies: an ALE meta-analysis. Neurosci Biobehav Rev, 2019, 103: 60-72.
28. Choe KY, Sanchez CF, Harris NG, et al. Optogenetic fMRI and electrophysiological identification of region-specific connectivity between the cerebellar cortex and forebrain. Neuroimage, 2018, 173: 370-383.
29. Watson TC, Obiang P, Torres-Herraez A, et al. Anatomical and physiological foundations of cerebello-hippocampal interaction. Elife, 2019, 17(8): e41896.

West China Medical Journal

Topology properties of spatial navigation-related functional brain networks in crowds: a study based on graph theory analysis

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