← Back to all news
arXiv Machine Learning By Siyuan Dai, Yang Du, Kun Zhao, Zhusuyi Chen, Heng Huang, Paul Thompson, Chao Shi, Haoteng Tang, Liang Zhan

Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS

Read the original on arXiv Machine Learning →

arXiv:2606. 18287v1 Announce Type: new Abstract: Multimodal neuroimaging, integrating functional connectivity from fMRI and structural connectivity from DTI, enables non-invasive analysis of brain networks using graph neural networks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jul 9

Latent graph encoding of multimodal neuroimaging features with generative AI architectures

arXiv:2607. 07027v1 Announce Type: cross Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encoding and latent space processes is crucial for studying structural and functional properties of the brain.

By Ishaan Batta, Meenu Ajith, Vince Calhoun
llmsdiffusionmultimodalsafety
More like this →
arXiv AI
Jun 2

Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes

arXiv:2606. 01237v1 Announce Type: new Abstract: Mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are closely associated with the early Alzheimer's disease continuum, where accurate and explainable diagnosis is important for early risk assessment and intervention.

By Xiongri Shen, Jiaqi Wang, Zhenxi Song, Yi Zhong, Leilei Zhao, Xin He, Baiying Lei, Zhiguo Zhang
llmsmultimodalbenchmarkssafety
More like this →
arXiv AI
3d ago

BrainNet Studio: A Unified Toolkit for Brain Network Construction, Intelligent Analysis, and Visualization

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
llms
More like this →