Identification of Mild Cognitive Impairment Subtypes using an Interpretable Neural Network based Clustering of Gene Expression Data and Neuroimaging Markers

In this study, an attempt is made to cluster the gene expression data and neuroimaging markers using an interpretable neural network model to identify Mild Cognitive Impairment (MCI) subtypes. For this, structural Magnetic Resonance (MR) brain images and gene expression data of early and late MCI subjects are considered from a public database. A neural network model is employed to cluster the gene expression data and regional MR volumes. To evaluate the performance of model, clustering metrics are employed and model is explained using perturbation-based method. Results indicate that the developed model is able to identify MCI subtypes. The network learns latent embeddings of disease-specific genes and MR images markers. The clustering metrics are found to be highest when both the imaging and genetic markers are employed. Volumes of lateral ventricles, hippocampus, amygdala and thalamus are found to be associated with late MCI. Significant scores suggest that genes such as StAR, CCDC108, APOO, TRMT13, RASAL2 and ZNF43 play a key role in identifying the MCI subtypes.Clinical Relevance—Identifying distinct MCI subtypes offer potential for precision diagnostics and targeted clinical recruitment.