Brain Network
Brain network research aims to understand the complex interactions between brain regions, using neuroimaging data to map functional and structural connectivity. Current research focuses on developing advanced machine learning models, including graph neural networks, transformers, and diffusion models, to analyze these networks, often incorporating multimodal data (e.g., fMRI, EEG) and addressing challenges like noise and data heterogeneity. These advancements are improving the accuracy of disease diagnosis (e.g., Alzheimer's, autism, schizophrenia) and providing deeper insights into brain function and cognitive processes, with implications for personalized medicine and treatment strategies.
Papers
Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders
Qianqian Wang, Wei Wang, Yuqi Fang, Hong-Jun Li, Andrea Bozoki, Mingxia Liu
Using Structural Similarity and Kolmogorov-Arnold Networks for Anatomical Embedding of 3-hinge Gyrus
Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Tianming Liu, Dajiang Zhu
Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks
Rui Sherry Shen, Yusuf Osmanlıoğlu, Drew Parker, Darien Aunapu, Benjamin E. Yerys, Birkan Tunç, Ragini Verma
Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks
Oktay Agcaoglu, Rogers F. Silva, Deniz Alacam, Sergey Plis, Tulay Adali, Vince Calhoun (for the Alzheimers Disease Neuroimaging Initiative)
Identifying Influential nodes in Brain Networks via Self-Supervised Graph-Transformer
Yanqing Kang, Di Zhu, Haiyang Zhang, Enze Shi, Sigang Yu, Jinru Wu, Xuhui Wang, Xuan Liu, Geng Chen, Xi Jiang, Tuo Zhang, Shu Zhang
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
Jiaxing Xu, Kai He, Mengcheng Lan, Qingtian Bian, Wei Li, Tieying Li, Yiping Ke, Miao Qiao