BrainNet Studio is a unified toolkit that enables the construction, analysis, and visualization of both static and dynamic brain networks. It integrates 27 algorithms—including deep learning, graph neural networks, and spatiotemporal sequence models—to support classification, biomarker identification, and the extraction of discriminative brain regions and connections. The toolkit also employs a large language model to generate researcher‑verifiable summaries of functional and structural connectivity, as well as structure‑function coupling, at individual and group levels.
By Xiwei Zeng, Shengrong Li, Yiheng Liu, Chunwei Tian, Daoqiang Zhang, Qi Zhu
arXiv:2607. 28681v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) is a widely used technique for studying the brain.
By Yang Zhang, Xiao Zhou, Jonathan Warrell, Avram Holmes, Xuan Zhang, Mark Gerstein
Brain networks exhibit a modular community structure that varies across individuals and neurological conditions. However, existing self-supervised learning (SSL) methods often overlook this heterogeneity, relying on generic masking strategies that fail to capture subject-specific functional organization.
arXiv:2608.20380v1 Announce Type: cross
Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) has enabled non-invasive mapping of functional brain interactions for computer-aided di...
By Dengyi Zhao, Zhiheng Zhou, Zihan Wang, Guiying Yan, Xingqin Qi
Functional brain networks exhibit a hierarchical organization across ROI, community, and whole-brain levels, supporting local processing, inter-community coordination, and global integration. Recent studies have demonstrated that brain community-aware modeling is beneficial for both diagnosis and biomarker identification of brain networks.
arXiv:2607. 07077v1 Announce Type: cross Abstract: Functional brain networks exhibit a hierarchical organization across ROI, community, and whole-brain levels, supporting local processing, inter-community coordination, and global integration.
By Yapeng Li, Bo Jiang, Ziyan Zhang, Dongdong Chen, Zhengzheng Tu
arXiv:2609.06093v1 Announce Type: new
Abstract: Connectomes, graph-level maps of neurons and their synaptic connections, provide a structural basis for understanding how brain circuits support functi...
By Zhuolin Yu, Xingyu Liu, Yuanhao Jia, Yunhang Xiao, Hairuo Xue, Feihan Sun, Guozhang Chen
arXiv:2511. 04539v2 Announce Type: replace-cross Abstract: In network neuroscience, functional brain systems are often characterized using separate yet related graph-theoretic or spectral descriptors, overlooking how these properties covary and partially overlap across individuals and conditions.
By Subati Abulikemu, Tiago Azevedo, Michail Mamalakis, John Suckling
arXiv:2608. 07393v1 Announce Type: new Abstract: Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to generalize well.
By Deepank Girish, Yi Hao Chan, Yubin Zheng, Sukrit Gupta, Jagath C. Rajapakse
The paper introduces Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework that extends Symmetric Nonnegative Matrix Tri-Factorization to supervised prediction across populations of multimodal brain graphs. SD3MF learns deep hierarchical factorizations for each modality and a shared latent representation, jointly optimizing graph reconstruction and prediction while enabling data-driven multimodal fusion. Experiments on multimodal connectome datasets demonstrate that SD3MF outperforms strong deep learning baselines such as CNNs and GNNs, providing biologically interpretable insights through community-level interaction matrices.
By Amjad Seyedi, Lifang He, Songlin Zhao, Akwum Onwunta, Nicolas Gillis
The paper introduces MSR-IVA, a state-aware fusion method that combines a shared structural representation with state‑specific residual adaptations and masks for incomplete state expression. Applied to an Alzheimer’s Disease Neuroimaging Initiative cohort, MSR-IVA increased matched source coupling by 6.5% and decreased unmatched dependence by 15.7% compared to a baseline IVA approach. For subjects expressing both states, the method achieved a mean absolute cross‑state structural source correlation of 0.9177, far higher than the 0.2978 obtained without sharing, indicating effective preservation of source correspondence while allowing state‑specific adaptation.
By Victor Solomon, Zening Fu, Rafal Angryk, Vince D. Calhoun, Jingyu Liu
arXiv:2606. 03310v1 Announce Type: cross Abstract: Understanding complex interactions between brain regions is critical for early neurodegenerative disease classification such as Alzheimer's Disease (AD) and Parkinson's Disease (PD).
By Jaeyoon Sim, Soojin Hwang, Seunghun Baek, Guorong Wu, Won Hwa Kim