摘要
Brain functional network (BFN) has become an important tool for the analysis and diagnosis of brain diseases, and how to build a high-quality BFN based on resting-state functional magnetic resonance imaging (rs-fMRI) has become a growing concern in the neuroscience community. Although some methods have been proposed to construct a high-quality BFN, they only encode the spatial characteristics of the ROIs, ignoring the temporal characteristics. As a result, it becomes challenging to accurately capture the true state of the brain. To address this problem, we propose a novel method to construct a higher-order BFN, considering both temporal and spatial domain characteristics. In particular, we get the characteristics of the temporal domain by differentiating the rs-fMRI signal itself, and then we integrate the information of the spatial domain and temporal domain to build a high-order BFN. To evaluate the proposed method, we conduct our experiments on ABIDE database to identify subjects with Autism Spectrum Disorder (ASD) from normal controls. Experimental results show that our method can achieve higher performance than baseline methods.
Brain functional network (BFN) has become an important tool for the analysis and diagnosis of brain diseases, and how to build a high-quality BFN based on resting-state functional magnetic resonance imaging (rs-fMRI) has become a growing concern in the neuroscience community. Although some methods have been proposed to construct a high-quality BFN, they only encode the spatial characteristics of the ROIs, ignoring the temporal characteristics. As a result, it becomes challenging to accurately capture the true state of the brain. To address this problem, we propose a novel method to construct a higher-order BFN, considering both temporal and spatial domain characteristics. In particular, we get the characteristics of the temporal domain by differentiating the rs-fMRI signal itself, and then we integrate the information of the spatial domain and temporal domain to build a high-order BFN. To evaluate the proposed method, we conduct our experiments on ABIDE database to identify subjects with Autism Spectrum Disorder (ASD) from normal controls. Experimental results show that our method can achieve higher performance than baseline methods.
作者
Mengxue Pang
Limei Zhang
Xueyan Liu
Tinglin Zhang
Shufeng Zhou
Mengxue Pang;Limei Zhang;Xueyan Liu;Tinglin Zhang;Shufeng Zhou(School of Mathematics Science, Liaocheng University, Liaocheng, China;School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China;School of Information Engineering, Yancheng Institute of Technology, Yancheng, China)
