Evidence map›Paper›PMID 40160628›Full record

ArticleQuantitative imaging in medicine and surgery2025

Connectomics modeling of regional networks of white-matter fractional anisotropy to predict the severity of young adult drinking.

Yashuang Li, Guangfei Li, Lin Yang, Yan Yan, Ning Zhang, Mengdi Gao, Dongmei Hao, Yiyao Ye-Lin, Chiang-Shan R Li

Abstract read
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In one paragraph

Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

9 authors.

Yashuang LiDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
Guangfei LiDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
Lin YangDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
Yan YanOffice of Academic Research, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Ning ZhangDepartment of Neuropsychiatry and Behavioral Neurology and Clinical Psychology, Sleep Center, Department of Neurology, China National Clinical Research Center of Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Mengdi GaoDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
Dongmei HaoDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
Yiyao Ye-LinBJUT-UPV Joint Research Laboratory in Biomedical Engineering, Beijing, China.
Chiang-Shan R LiDepartment of Psychiatry and Department of Neuroscience, Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Alcohol use impacts brain structure, including white matter integrity, which can be quantified by fractional anisotropy (FA) in diffusion tensor imaging (DTI). This study explored the relationship between the severity of alcohol consumption and white matter FA changes, and its sex differences, in young adults, using data from the Human Connectome Project. Methods: We analyzed DTI data from 949 participants (491 females) and used principal component analysis (PCA) of 15 drinking metrics to quantify drinking severity. Connectome-based predictive modeling (CPM) was employed to predict the principal component of drinking severity from network FA values in a matrix of 116×116 regions. Mediation analyses were conducted to explore the interrelationships among networks identified by CPM, drinking severity, and rule-breaking behavior. Results: Significant correlations were found between drinking severity and network FA values. Both men and women showed significant correlations between negative network connectivity and drinking severity (men: r=0.15, P=0.001; women: r=0.30, P<0.001). Sex differences were observed in the brain regions contributing to drinking severity predictions. Mediation analyses revealed significant inter-relationships between network features, drinking severity, and rule-breaking behavior. Conclusions: The connectomics of white matter FA can predict the severity of alcohol consumption, and by incorporating brain network pathways, identify sex differences. This approach provides new clues to the biological basis of alcohol abuse and evaluates how these regions interact in broader brain networks for understanding alcohol misuse and its comorbidities.

Indexed as

alcohol misuseAlcohol use disorder (AUD)connectomediffusion tensor imaging (DTI)

Identifiers

PMID40160628
PMCPMC11948382

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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 Socratic graph.