Evidence map›Paper›PMID 42262704›Full record

ArticleNeuroscience bulletin2026

Measuring the Impacts of Urbanicity and Different Exposome Factors on Human Brain through Exposure Network Mapping.

Na Luo, Zhengyi Yang, Ming Song, Shiqi Di, Congying Chu, Weiyang Shi, Weihua Yue, Yuyanan Zhang, Hao Yan, Xiao Zhang and 4 more

Abstract read
In one paragraph

Article in Neuroscience bulletin, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Na LuoBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Zhengyi YangBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Ming SongBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Shiqi DiBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Congying ChuBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Weiyang ShiBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Weihua YuePeking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
Yuyanan ZhangPeking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
Hao YanPeking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
Xiao ZhangPeking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
Dai ZhangPeking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
Jing SuiIDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Vince CalhounTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA.
Tianzi JiangBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. jiangtz@nlpr.ia.ac.cn.ORCID http://orcid.org/0000-0001-9531-291X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While urbanicity increases the risk of mental health issues, its effects on brain networks are heterogeneous and underexplored in relation to different exposome factors. Using a coordinate network mapping strategy termed exposure network mapping (ENM) across eight datasets, this study first consolidated heterogeneous findings of urbanicity to a significant, replicable network involving the middle frontal gyrus, orbital gyrus, and anterior cingulate gyrus. Afterwards, among the other factors examined (air pollution, noise, income, stress, green space), only stress converged into a distinct common network, highlighting the orbital gyrus, caudate, anterior/middle cingulate gyrus, hippocampus, and middle frontal gyrus. This ENM-stress map exhibited the highest correlation with both the ENM-urbanicity map (r = 0.77) and a transdiagnostic map (r = 0.72). In addition, sleep-related coordinates also formed a consistent network, involving the middle cingulate gyrus, orbital gyrus, caudate, and putamen, which correlated strongly with urbanicity (r = 0.75), stress (r = 0.80), and the transdiagnostic pattern (r = 0.55). Collectively, this study highlights the potential risks of urbanicity and stress, as well as the protective role of sleep on brain networks, which may offer new insights for preventing mental health issues in urban environments.

Indexed as

BrainBrain MappingExposomeNerve NetUrban PopulationAdultFemaleHumansMagnetic Resonance ImagingMaleNeural PathwaysSleepStress, PsychologicalExposomeHuman brain networksPsychiatrySleepStressUrbanicity

Identifiers

PMID42262704
PMCPMC13642883

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.