Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 243, Issue -, Pages -Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2023.107905
Keywords
End-stage renal disease; Mild cognitive impairment; Topological manifold; Hypergraph; Multi -modal feature selection
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This study proposed a multi-modal feature selection framework for classifying end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) patients and identifying discriminative brain regions. Brain networks were constructed using structural and functional imaging data, and node efficiency and clustering coefficient were extracted as features. The results showed that this framework outperformed existing methods in terms of diagnostic accuracy.
Background and Objective: The diagnosis of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) mainly relies on objective cognitive assessment, clinical observation, and neuro-psychological evaluation, while only adopting clinical tools often limits the diagnosis accuracy. Methods: We proposed a multi-modal feature selection framework with higher-order correlated topological manifold (HCTMFS) to classify ESRDaMCI patients and identify the discriminative brain regions. It constructed brain structural and functional networks with diffuse kurtosis imaging (DKI) and functional magnetic resonance imaging (fMRI) data, and extracted node efficiency and clustering coefficient from the brain networks to construct multi-modal feature matrices. The topological relationship matrices were constructed to measure the lower-order topological correlation between features. Then the consensus matrices were learned to approximate the topological relationship matrices at different confidence levels and eliminate the noise influence of individual matrices. Results: The higher-order topological correlation between features was explored by the Laplacian matrix of the hypergraph, which was calculated through the consensus matrix. The new framework achieved an accuracy rate of 93.56 % for classifying ESRDaMCI patients, and outperformed the existing state-of-the-art methods in terms of sensitivity, specificity, and area under the curve. Conclusions: This study contributes to effectively reflect the functional neural degradation of ESRDaMCI and provide a reference for the diagnosis of ESRDaMCI by selecting discriminative brain regions.
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