Evidence map›Paper›PMID 39639334›Full record

ArticleBioData mining2024

TGNet: tensor-based graph convolutional networks for multimodal brain network analysis.

Zhaoming Kong, Rong Zhou, Xinwei Luo, Songlin Zhao, Ann B Ragin, Alex D Leow, Lifang He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioData mining, 2024. 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. 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

7 authors.

Zhaoming Kong *School of Software Engineering, South China University of Technology, 382 Waihuan Dong Road, Guangzhou, 510006, China.
Rong Zhou *Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, 18015, PA, USA.
Xinwei LuoDepartment of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, 18015, PA, USA.
Songlin ZhaoDepartment of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, 18015, PA, USA.
Ann B RaginDepartment of Radiology, Northwestern University, 737 N. Michigan Avenue, Chicago, 60611, IL, USA.
Alex D LeowDepartment of Psychiatry, University of Illinois Chicago, 1601 W. Taylor Street, Chicago, 60612, IL, USA.
Lifang HeDepartment of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, 18015, PA, USA. lih319@lehigh.edu.

Funding

A Multiparametric MR Study of Early and Late Stage HIV InfectionR01MH080636 · NIMH · NORTHWESTERN UNIVERSITY AT CHICAGO · PI RAGIN, ANN B · 2007 to 2011
$1.6M
Surrogate Augmented Deep Predictive Learning for Retinopathy of PrematurityR21EY034179 · NEI · UNIVERSITY OF PENNSYLVANIA · PI CHEN, YONG, HE, LIFANG · 2023 to 2023
$482k
Lehigh University S00010293National Science Foundation MRI-2215789NEI NIH HHS R21 EY034179NIH HHS R01MH080636NIH HHS R21EY034179NIMH NIH HHS R01 MH080636
6 · The paper itself

Abstract

Multimodal brain network analysis enables a comprehensive understanding of neurological disorders by integrating information from multiple neuroimaging modalities. However, existing methods often struggle to effectively model the complex structures of multimodal brain networks. In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. We evaluate TGNet on four datasets-HIV, Bipolar Disorder (BP), and Parkinson's Disease (PPMI), Alzheimer's Disease (ADNI)-demonstrating that it significantly outperforms existing methods for disease classification tasks, particularly in scenarios with limited sample sizes. The robustness and effectiveness of TGNet highlight its potential for advancing multimodal brain network analysis. The code is available at  https://github.com/rongzhou7/TGNet .

Indexed as

Disease classificationGraph convolutional networkMultimodal brain networksTensor

Identifiers

PMID39639334
PMCPMC11622555

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.