Modular constraint-based joint dynamic functional brain graph learning model for identifying brain disorders
4 affiliations
- 1Shandong Sport University, Jinan, Shandong, China
- 2School of Physical Education, Shandong University, Jinan, Shandong, China
- 3School of Computer and Artificial Intelligence, Shandong Jianzhu University, Jinan, Shandong, China
- 4School of Mathematics Science, Liaocheng University, Liaocheng, Shandong, China
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) captures spontaneous neural activity and has become a valuable tool for investigating functional alterations in brain disorders. A widely used strategy is to estimate functional connectivity (FC) between brain regions based on blood-oxygen-level-dependent (BOLD) signals, which can be further represented as functional brain graphs (FBGs). Despite promising results, existing methods typically rely on static FBGs derived from a single brain atlas, which limits their ability to capture the temporal variability of brain activity and integrate complementary anatomical information. Moreover, the modular organization of brain networks-an important feature linked to cognitive function and disease-has often been overlooked in model design. To address these limitations, we propose a modularity-constrained dynamic FBG learning framework for brain disorder classification. The proposed method integrates FBGs constructed from multiple brain atlases and captures dynamic spatio-temporal patterns using a sliding window strategy and a Transformer encoder. We employ a stochastic block model (SBM) to detect modular structures, and further incorporate modularity constraints into the training objective to enhance model interpretability and regularize learning. Experiments on the Autism Brain lmaging Data Exchange (ABIDE) and the REST-meta-MDD Consortium (REST-MDD) datasets demonstrate that our method achieves classification accuracies of 68.5% and 65.3%, respectively, outperforming conventional and competitive deep learning methods. This framework provides a novel perspective for dynamic modeling of brain functional connectivity and demonstrates potential for clinical application.
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BibTeX
@article{xu2026modular,
title = {{Modular constraint-based joint dynamic functional brain graph learning model for identifying brain disorders}},
author = {Peiming Xu and Yaru Li and Wei Si and Linmin Wang and Xiao Jiang},
journal = {PeerJ},
year = {2026},
month = sep,
doi = {10.7717/peerj.21439},
url = {https://peerj.com/articles/21439},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Modular constraint-based joint dynamic functional brain graph learning model for identifying brain disorders
AU - Peiming Xu
AU - Yaru Li
AU - Wei Si
AU - Linmin Wang
AU - Xiao Jiang
PY - 2026
DA - 2026/09/30
JO - PeerJ
DO - 10.7717/peerj.21439
UR - https://peerj.com/articles/21439
AB - Resting-state functional magnetic resonance imaging (rs-fMRI) captures spontaneous neural activity and has become a valuable tool for investigating functional alterations in brain disorders. A widely used strategy is to estimate functional connectivity (FC) between brain regions based on blood-oxygen-level-dependent (BOLD) signals, which can be further represented as functional brain graphs (FBGs). Despite promising results, existing methods typically rely on static FBGs derived from a single brain atlas, which limits their ability to capture the temporal variability of brain activity and integrate complementary anatomical information. Moreover, the modular organization of brain networks-an important feature linked to cognitive function and disease-has often been overlooked in model design. To address these limitations, we propose a modularity-constrained dynamic FBG learning framework for brain disorder classification. The proposed method integrates FBGs constructed from multiple brain atlases and captures dynamic spatio-temporal patterns using a sliding window strategy and a Transformer encoder. We employ a stochastic block model (SBM) to detect modular structures, and further incorporate modularity constraints into the training objective to enhance model interpretability and regularize learning. Experiments on the Autism Brain lmaging Data Exchange (ABIDE) and the REST-meta-MDD Consortium (REST-MDD) datasets demonstrate that our method achieves classification accuracies of 68.5% and 65.3%, respectively, outperforming conventional and competitive deep learning methods. This framework provides a novel perspective for dynamic modeling of brain functional connectivity and demonstrates potential for clinical application.
SN - 2167-8359
ER -