Evidence map›Paper›PMID 41034549›Full record

ArticleNeuroscience bulletin2026

Physical Activity and Depressive Mood Share the Structural Connectivity Between Motor and Reward Networks.

Shiqi Di, Na Luo, Weiyang Shi, Zhengyi Yang, Jing Sui, Rongtao Jiang, Yue Cui, Zongchang Du, Jiaqi Zhang, Yawei Ma and 11 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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

21 authors.

Shiqi Di *Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Na Luo *Beijing 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.
Zhengyi YangBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.ORCID http://orcid.org/0000-0002-7819-439X
Jing SuiIDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.ORCID http://orcid.org/0000-0001-6837-5966
Rongtao JiangDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA.
Yue CuiBrainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.ORCID http://orcid.org/0000-0002-5001-4940
Zongchang DuBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Jiaqi ZhangBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Yawei MaBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Haiyan WangBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.ORCID http://orcid.org/0000-0003-1107-6679
Congying ChuBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.ORCID http://orcid.org/0000-0002-7808-8896
Yuejia ZhongBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Wen LiBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Yuheng LuBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Hao YanPeking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.ORCID http://orcid.org/0000-0003-0376-9037
Jinmin LiaoPeking 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.ORCID http://orcid.org/0000-0002-9162-7401
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.
Ming SongBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
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

In various studies, exercise has been revealed to have a positive effect on alleviating depressive symptoms. However, the neural basis behind this phenomenon remains unknown, as well as its underlying biological mechanism. In this study, we used a large neuroimaging cohort [n = 1,027, major depressive disorder (MDD)/healthy controls (HCs) = 492/535] from the UK Biobank to identify structural connectivity (SC) patterns simultaneously linked with physical activity and depression, as well as the biological interpretation. An SC pattern linked with exercise was identified to be both significantly correlated with depressive mood and group discrimination between MDDs and HCs, primarily located between the motor-related regions and reward-related regions. This pattern was associated with multiple neurotransmitter receptors, such as serotonin and GABA receptors, and enriched in pathways like synaptic signaling and the astrocyte cell type. The SC pattern and genetic results were also replicated in another independent MDD dataset (n = 3,496) and present commonalities with bipolar disorder (n = 81). Overall, these findings not only initially identified a reproducible shared SC pattern between physical activity and depressive mood, but also elucidated the underlying biological mechanisms, which enhance our understanding of how exercise helps alleviate depression and may inform the development of novel neuromodulation targets.

Indexed as

BrainExerciseMajor Depressive DisorderNerve NetRewardAdultAffectFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeural PathwaysBiological mechanismsDepressionPhysical activityReplicable associationStructural connectivity

Identifiers

PMID41034549
PMCPMC13031685

What OpenQuestion holds

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Registered trials

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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.