Evidence map›Paper›PMID 40549161›Full record

ArticleBrain topography2025

BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine Clinical Brain Structural T1-Weighted Sequence.

Haiwang Nan, Zhiwei Song, Qiang Zheng

Abstract read
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In one paragraph

Article in Brain topography, 2025. 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

3 authors.

Haiwang NanSchool of Computer and Control Engineering, Yantai University, NO30, Qingquan Road, Laishan District, 264005, Yantai, China.
Zhiwei SongSchool of Computer and Control Engineering, Yantai University, NO30, Qingquan Road, Laishan District, 264005, Yantai, China.
Qiang ZhengSchool of Computer and Control Engineering, Yantai University, NO30, Qingquan Road, Laishan District, 264005, Yantai, China. zhengqiang@ytu.edu.cn.

Funding

Open Project of Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University YXZN2022002Shandong Provincial Natural Science Foundation ZR2020QH048the National Natural Science Foundation of China 61802330, 61802331Yantai City Science and Technology Innovation Development Plan 2023XDRH006
6 · The paper itself

Abstract

Functional brain network (FBN) derived from functional Magnetic Resonance Imaging (fMRI) has promising prospects in clinical research, but fMRI is not a routine acquisition data, which limits its popularity in clinical applications. Therefore, it is imperative to generate FBN based on routine clinical structural MRI brain network. In this study, a BrainNet-GAN model was proposed for generating FBN from radiomics-based morphological brain network (radMBN) derived from routinely acquired T1-weighted image (T1WI). BrainNet-GAN integrated two Multi-Channel Multi-Scale Adaptive (Multi

Indexed as

BrainBrain MappingMagnetic Resonance ImagingNerve NetNeural Networks, ComputerAdultFemaleHumansImage Processing, Computer-AssistedMaleFunctional magnetic resonance imagingGenerative adversarial networksGraph convolutional networkRadiomics FeaturesT1-weighted image

Identifiers

PMID40549161

What OpenQuestion holds

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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 OpenQuestion graph.