Evidence map›Paper›PMID 40100543›Full record

ArticleNeuroscience bulletin2025

Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.

Jingjing Gao, Heping Tang, Zhengning Wang, Yanling Li, Na Luo, Ming Song, Sangma Xie, Weiyang Shi, Hao Yan, Lin Lu and 18 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuroscience bulletin, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026
    Review
  2. Article
  3. 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

28 authors.

Jingjing GaoSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Heping TangSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Zhengning WangSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Yanling LiSchool of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China.
Na LuoBeijing 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.
Sangma XieInstitute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
Weiyang ShiBeijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
Hao YanInstitute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China.
Lin LuInstitute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China.
Jun YanInstitute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China.
Peng LiInstitute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China.
Yuqing SongInstitute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, China.
Jun ChenDepartment of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
Yunchun ChenDepartment of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, 710032, China.
Huaning WangDepartment of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, 710032, China.
Wenming LiuDepartment of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, 710032, China.
Zhigang LiZhumadian Psychiatric Hospital, Zhumadian, 463000, China.
Hua GuoZhumadian Psychiatric Hospital, Zhumadian, 463000, China.
Ping WanZhumadian Psychiatric Hospital, Zhumadian, 463000, China.
Luxian LvDepartment of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, China.
Yongfeng YangDepartment of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, China.
Huiling WangDepartment of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
Hongxing ZhangDepartment of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, China.
Huawang WuThe Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.
Yuping NingThe Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, 510370, China.
Dai ZhangInstitute of Mental Health, Peking University Sixth Hospital, Beijing, 100191, 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

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.

Indexed as

BrainDiffusion Tensor ImagingNerve NetSchizophreniaAdultFemaleGene ExpressionGraph Neural NetworksHumansMaleMiddle AgedWhite MatterYoung AdultClassificationDeep learningGraph neural networkMagnetic resonance imagingSchizophrenia

Identifiers

PMID40100543
PMCPMC12158903

What OpenQuestion holds

Textmetadata
Read underepoch 390

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 OpenQuestion graph.