no code implementations • 14 Dec 2023 • Qiankun Zuo, Ling Chen, Shuqiang Wang
It can captures both unidirectal and bidirectional interactions between brain regions, providing a comprehensive understanding of the brain's information processing mechanisms.
no code implementations • 28 Sep 2023 • Qiankun Zuo, Junren Pan, Shuqiang Wang
The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner.
no code implementations • 28 Sep 2023 • Qiankun Zuo, Ruiheng Li, Yi Di, Hao Tian, Changhong Jing, Xuhang Chen, Shuqiang Wang
In this paper, a novel diffusision generative adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict SC from brain fMRI in an end-to-end manner.
no code implementations • 16 Jun 2023 • Qiankun Zuo, Yanfei Zhu, Libin Lu, Zhi Yang, Yuhui Li, Ning Zhang
In this paper, a novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices and predict abnormal brain connections based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI).
no code implementations • 23 May 2023 • Qiankun Zuo, Baiying Lei, Ning Zhong, Yi Pan, Shuqiang Wang
Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically.
no code implementations • 18 May 2023 • Qiankun Zuo, Chi-Man Pun, Yudong Zhang, Hongfei Wang, Jin Hong
In this paper, a novel Multi-resolution Spatiotemporal Enhanced Transformer Denoising (MSETD) network with an adversarially functional diffusion model is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment (MCI) analysis.
no code implementations • 9 Aug 2022 • Yongcheng Zong, Changhong Jing, Qiankun Zuo
The application of machine learning algorithms to the diagnosis and analysis of Alzheimer's disease (AD) from multimodal neuroimaging data is a current research hotspot.
no code implementations • 12 Oct 2021 • Qiankun Zuo, Baiying Lei, Shuqiang Wang, Yong liu, BingChuan Wang, Yanyan Shen
The proposed model can evaluate characteristics of abnormal brain connections at different stages of Alzheimer's disease, which is helpful for cognitive disease study and early treatment.
no code implementations • 21 Jul 2021 • Qiankun Zuo, Baiying Lei, Yanyan Shen, Yong liu, Zhiguang Feng, Shuqiang Wang
Then two hypergraphs are constructed from the latent representations and the adversarial network based on graph convolution is employed to narrow the distribution difference of hyperedge features.