Qiankun Zuo^1,4, Junhua Hu², Yudong Zhang^3,*, Junren Pan⁴, Changhong Jing⁴, Xuhang Chen⁵, Xiaobo Meng⁶, Jin Hong^7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2129-2147, 2023, DOI:10.32604/cmes.2023.028732

Abstract The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders. The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders. However, it is challenging to access considerable amounts of brain functional network data, which hinders the widespread application of data-driven models in dementia diagnosis. In this study, a novel distribution-regularized adversarial graph auto-Encoder (DAGAE) with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset, improving the dementia diagnosis accuracy of data-driven models. Specifically, the label distribution… More > Graphic Abstract

Zhengtao Xi¹, Chaofan Song², Jiahui Zheng³, Haifeng Shi³, Zhuqing Jiao^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2243-2266, 2023, DOI:10.32604/cmes.2023.023544

Abstract The structure and function of brain networks have been altered in patients with end-stage renal disease (ESRD). Manifold regularization (MR) only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions. To solve this issue, we developed a method to construct a dynamic brain functional network (DBFN) based on dynamic hypergraph MR (DHMR) and applied it to the classification of ESRD associated with mild cognitive impairment (ESRDaMCI). The construction of DBFN with Pearson’s correlation (PC) was transformed into an optimization model. Node convolution and hyperedge convolution superposition were adopted to… More > Graphic Abstract

Zhuqing Jiao^{1, *}, Yixin Ji¹, Tingxuan Jiao¹, Shuihua Wang^{2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 845-871, 2020, DOI:10.32604/cmes.2020.08999

Abstract Currently, functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders. If one brain disease just manifests as some cognitive dysfunction, it means that the disease may affect some local connectivity in the brain functional network. That is, there are functional abnormalities in the sub-network. Therefore, it is crucial to accurately identify them in pathological diagnosis. To solve these problems, we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization (GNMF). The dynamic functional networks of normal subjects and early mild cognitive impairment (eMCI) subjects were vectorized and the functional connection… More >