Li, Y., Liu, J., Tang, Z. and Lei, B. (2020) Deep Spatial-Temporal Feature Fusion from Adaptive Dynamic Functional Connectivity for MCI Identification. IEEE Transactions on Medical Imaging, 39, 2818-2830.
https://doi.org/10.1109/TMI.2020.2976825

has been cited by the following article:

• TITLE: Estimating Brain Functional Networks Based on Spatiotemporal Higher-Order Correlations for Autism Identification

  AUTHORS: Mengxue Pang, Limei Zhang, Xueyan Liu, Tinglin Zhang, Shufeng Zhou

  KEYWORDS: Functional MRI, HOFN, Difference, Autism Spectrum Disorder

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.8, August 30, 2023

  ABSTRACT: 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.