Evidence map›Paper›PMID 38774602›Full record

ArticleInformation processing in medical imaging : proceedings of the ... conference2023

Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data.

Jinghan Huang, Moo K Chung, Anqi Qiu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Information processing in medical imaging : proceedings of the ... conference, 2023. 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. Article
  2. A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025
    Article
  3. 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

3 authors.

Jinghan HuangDepartment of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
Moo K ChungDepartment of Biostatistics and Medical Informatics, The University of Wisconsin-Madison, Wisconsin, USA.
Anqi QiuDepartment of Biomedical Engineering, National University of Singapore, Singapore, Singapore.

Funding

Dynamic embedding time series models in functional brain imagingR01MH133614 · NIMH · UNIVERSITY OF WISCONSIN-MADISON · PI MOO K CHUNG · 2023 to 2026
$1.4M
BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the BrainR01EB022856 · NIBIB · UNIVERSITY OF WISCONSIN-MADISON · PI CHUNG, MOO K · 2016 to 2018
$1.1M
Dynamic manifold-valued time series model in functional brain imagingR01EB028753 · NIBIB · UNIVERSITY OF WISCONSIN-MADISON · PI CHUNG, MOO K · 2020 to 2022
$942k
NIBIB NIH HHS R01 EB022856NIBIB NIH HHS R01 EB028753NIMH NIH HHS R01 MH133614
6 · The paper itself

Abstract

This study proposes a novel heterogeneous graph convolutional neural network (HGCNN) to handle complex brain fMRI data at regional and across-region levels. We introduce a generic formulation of spectral filters on heterogeneous graphs by introducing the

Identifiers

PMID38774602
PMCPMC11108189

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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.