Effects of Renal Function on the Multimodal Brain Networks Affecting Mild Cognitive Impairment Converters in End-Stage Renal Disease

Abstract

Rationale and Objectives

Cognitive decline is common in End-Stage Renal Disease (ESRD) patients, yet its neural mechanisms are poorly understood. This study investigates structural and functional brain network reconfiguration in ESRD patients transitioning to Mild Cognitive Impairment (MCI) and evaluates its potential for predicting MCI risk.

Methods

We enrolled 90 ESRD patients with 2-year follow-up, categorized as MCI converters (MCI_C, n=48) and non-converters (MCI_NC, n=42). Brain networks were constructed using baseline rs-fMRI and high angular resolution diffusion imaging, focusing on regional structural-functional coupling (SFC). A Support Vector Machine (SVM) model was used to identify brain regions associated with cognitive decline. Mediation analysis was conducted to explore the relationship between kidney function, brain network reconfiguration, and cognition.

Results

MCI_C patients showed decreased network efficiency in the structural network and compensatory changes in the functional network. Machine learning models using multimodal network features predicted MCI with high accuracy (AUC=0.928 for training set, AUC=0.903 for test set). SHAP analysis indicated that reduced hippocampal SFC was the most significant predictor of MCI_C. Mediation analysis revealed that altered brain network topology, particularly hippocampal SFC, mediated the relationship between kidney dysfunction and cognitive decline.

Conclusion

This study provides new insights into the link between kidney function and cognition, offering potential clinical applications for structural and functional MRI biomarkers.