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Accurately modeling resting-brain functional connectivity using hypergraph neural field-Fourier deep neural network.

Overview

Authors: Jichao Ma1, Jiebin Luo2, Dandan Liu3, Xi’an Li4
  1. School of Intelligent Manufacturing and Materials, Qingdao Binhai University, Qingdao, 266555 China
  2. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731 China
  3. School of Art and Science, Qingdao Binhai University, Qingdao, 266555 China
  4. Shandong University, Qingdao, 266237 China
Institutions: Qingdao Binhai University (China); University of Electronic Science and Technology of China (China); Shandong University (China)
Journal: Scientific reports, volume 16, issue 1, article 25719
Dates: received 1 October 2025; accepted 10 June 2026; published online 15 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-57930-3 · PMID 42297894 · PMCID PMC13482610 · OpenAlex W7164851627
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: structural MRI / diffusion (modality), fMRI (modality), human (organism), computational (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, fMRI & imaging, Machine learning
Keywords: Structural connectivity, Functional connectivity, Hypergraph neural field, Negative correlation, Deep neural network, Computational biology and bioinformatics, Neuroscience
MeSH: Brain*, Connectome*, Models, Neurological*, Nerve Net*, Neural Networks, Computer*, Rest*, Brain Mapping, Graph Neural Networks, Humans, Magnetic Resonance Imaging (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Doctoral Fund of Qingdao Binhai University (BS2024A009)
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Data

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Data availability statement

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Read it in the paper: doi.org/10.1038/s41598-026-57930-3.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 keywords, 10 MeSH terms, 1 funder, 44 references.

Cite

This paper

Ma, J., Luo, J., Liu, D., & Li, X. (2026). Accurately modeling resting-brain functional connectivity using hypergraph neural field-Fourier deep neural network. Scientific reports, 16(1), 25719. https://doi.org/10.1038/s41598-026-57930-3

BibTeX

@article{ma2026accurately,
author = {Ma, Jichao and Luo, Jiebin and Liu, Dandan and Li, Xi’an},
title = {{Accurately modeling resting-brain functional connectivity using hypergraph neural field-Fourier deep neural network}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {25719},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-57930-3},
url = {https://doi.org/10.1038/s41598-026-57930-3},
pmid = {42297894},
pmcid = {PMC13482610}
}

RIS

TY - JOUR
AU - Ma, Jichao
AU - Luo, Jiebin
AU - Liu, Dandan
AU - Li, Xi’an
TI - Accurately modeling resting-brain functional connectivity using hypergraph neural field-Fourier deep neural network
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/15
VL - 16
IS - 1
SP - 25719
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-57930-3
UR - https://doi.org/10.1038/s41598-026-57930-3
LA - en
ER -

CSL-JSON

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