Measuring the impacts of urbanicity and different exposure factors on human brain networks
Poster No:
550
Submission Type:
Abstract Submission
Authors:
Na Luo1, Zhengyi Yang1, Ming Song1, Shiqi Di1, Congying Chu1, Weiyang Shi1, Jing Sui2, Vince Calhoun3, Tianzi Jiang1
Institutions:
1Institute of automation, Chinese academy of sciences, Beijing, China, 2Beijing Normal University, Beijing, China, 3GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Accumulating evidence has shown that urbanicity could affect brain structure and function, and even carry a higher risk of experiencing mental health issues(Cheng et al., 2021; Fett, Lemmers-Jansen, & Krabbendam, 2019; Nations, 2018). However, the reported brain regions are largely inconsistent across studies and the differences between various exposure factors in urban environments have yet to be explored. In this work, we introduced a new technique termed exposure network mapping (ENM) to examine whether existed findings localize to a common network for urbanicity and its relationship with different exposure factors, as well as the association with mental health.
Methods:
We searched the PubMed and Web of Science bibliographic databases through 29 May 2023. For urbanicity, search terms consisted of '((urbanicity OR urban OR urbanization) AND (magnetic resonance imaging OR neuroimaging OR grey matter OR voxel OR cortical) AND (brain) AND (human))'. With the MNI coordinates reported in each selected urbanicity study, we conducted ENM analysis according to the following steps (Figure 1A). First, a 3-mm-radius sphere centered on each coordinate reported in each study was created. We then merged these spheres of the same study to obtain a combined seed. A normative connectome of healthy controls from the Genome Superstruct Project (GSP)(Holmes et al., 2015; Yeo et al., 2011) was used to compute the resting-state functional connectivity between each study-level combined seed and the rest of the brain. Then we followed Peng et al.'s complementary 't-test' approach to combine the study-level maps (Peng et al., 2022). The resulting group-level T-map was named as ENM-urbanicity map. When measuring the impacts of different exposure factors on human brain networks, we selected air pollution, noise pollution, stress, family incomes, sleep and greenspace as the representative exposure factors for each of the four exposure categories(Vermeulen et al., 2020). After a thorough literature search, we applied the same pipeline used for urbanicity to compute the ENM results for each exposure factor. We further calculated the spatial correlation between the derived ENM maps of each exposure factor and urbanicity. Finally, we computed the spatial correlation between a recently published transdiagnostic network (Taylor et al., 2023) for six psychiatric illnesses and the ENM-urbanicity map, as well as the derived ENM map of six exposure factors.
Results:
Utilizing the ENM strategy, we first determined a replicable common network for urbanicity across studies, highlighting caudate, putamen, orbitofrontal gyrus, insula, thalamus and the visual network (Figure 1B). Using the Neurosynth database and neuromaps toolbox (Markello et al., 2022; Yarkoni et al., 2011), it primarily interpreted the reward and emotion function, and correlated with dopamine and serotonin receptors (Figure 1C-D). We then computed ENM analysis on each of six selected exposure factors (Figure 2A). Only the ENM-stress map passed the cluster-level FWE correlation with P<0.05 (voxel-level P<0.001), which highlighted superior frontal gyrus, orbitofrontal gyrus, anterior cingulate gyrus, caudate, putamen and the visual network (Figure 2B). The derived maps of sleep exhibited the highest spatial correlation (r=0.78) with urbanicity, followed by stress, incomes and greenspace (Figure 2C). Finally, urbanicity (r=0.71) and social exposure factors, especially the stress (r=0.82), exhibited a high spatial association with a transdiagnostic map for six psychiatric disorders (Figure 2D-E).
Conclusions:
This study introduces a new technique termed ENM to systematically investigate the influences of urbanicity and six representative environmental factors on human brain networks. These results provide important hints that maintaining good sleep habits, increasing exposure to greenspace, and managing stress may help protect individuals from experiencing mental health issues in the context of rapid urbanization trends.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Data analysis
FUNCTIONAL MRI
Psychiatric Disorders
1|2Indicates the priority used for review
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Provide references using APA citation style.
Fett, A. J., Lemmers-Jansen, I. L. J., & Krabbendam, L. (2019). Psychosis and urbanicity: a review of the recent literature from epidemiology to neurourbanism. Curr Opin Psychiatry, 32(3), 232-241. doi:10.1097/yco.0000000000000486
Holmes, A. J., Hollinshead, M. O., O'Keefe, T. M., Petrov, V. I., Fariello, G. R., Wald, L. L., . . . Buckner, R. L. (2015). Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Sci Data, 2, 150031. doi:10.1038/sdata.2015.31
Markello, R. D., Hansen, J. Y., Liu, Z. Q., Bazinet, V., Shafiei, G., Suárez, L. E., . . . Misic, B. (2022). neuromaps: structural and functional interpretation of brain maps. Nat Methods, 19(11), 1472-1479. doi:10.1038/s41592-022-01625-w
Nations, U. (2018). World Urbanization Prospects.
Peng, S., Xu, P., Jiang, Y., & Gong, G. (2022). Activation network mapping for integration of heterogeneous fMRI findings. Nature Human Behaviour, 6(10), 1417-1429. doi:10.1038/s41562-022-01371-1
Taylor, J. J., Lin, C., Talmasov, D., Ferguson, M. A., Schaper, F., Jiang, J., . . . Fox, M. D. (2023). A transdiagnostic network for psychiatric illness derived from atrophy and lesions. Nat Hum Behav, 7(3), 420-429. doi:10.1038/s41562-022-01501-9
Vermeulen, R., Schymanski, E. L., Barabasi, A. L., & Miller, G. W. (2020). The exposome and health: Where chemistry meets biology. Science, 367(6476), 392-396. doi:10.1126/science.aay3164
Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nat Methods, 8(8), 665-670. doi:10.1038/nmeth.1635
Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., . . . Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125-1165. doi:10.1152/jn.00338.2011