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Multiplex functional connectome graph transformer for cognitive vulnerability in dialysis-treated chronic kidney
Junjie Zhai1, Jiachang Pang1, Xiangdong Lei1
1School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, China.
Objective:
Chronic kidney disease (CKD) requiring maintenance dialysis is accompanied by cognitive vulnerability and distributed brain-network dysfunction. This study evaluated whether complementary resting-state functional magnetic resonance imaging (rs-fMRI) connectome views could support subject-level characterization of CKD-related neurocognitive brain dysfunction.
Methods:
We analyzed a de-identified rs-fMRI dataset of 100 participants, including 52 patients with CKD receiving maintenance dialysis and 48 normal controls (NCs). The CKD group had lower Mini-Mental State Examination and Montreal Cognitive Assessment scores. AAL116 regional time series were represented as three 116 × 116 graph views: Pearson correlation, sparse representation, and Granger causality mapping. We developed MTGAF, a multiplex graph transformer combining modality-specific graph encoding, graph-transformer refinement, disease-aware readout, cross-modal attention, and adaptive fusion. Performance was evaluated with participant-level stratified 5-fold cross-validation and compared with BrainNetTF, BrainGNN, SVM, Random Forest, single-modality variants, and additional component-ablation variants. Robustness was assessed using pooled confusion matrices, bootstrap confidence intervals, calibration summaries, paired comparisons, and ROI importance analysis.
Results:
MTGAF achieved accuracy 0.8800 ± 0.0678, macro-F1 0.8796 ± 0.0680, and AUC 0.9297 ± 0.0437. The pooled confusion matrix contained 42 true negatives, 46 true positives, 6 false positives, and 6 false negatives. Bootstrap 95% CIs were 0.8100-0.9400 for accuracy, 0.8095-0.9396 for macro-F1, and 0.8696-0.9770 for pooled AUC. The full PC-SR-GCM model achieved higher accuracy and macro-F1 than single-modality variants. ROI importance highlighted cingulate, temporoparietal, sensorimotor, visual-association, frontostriatal, posterior cingulate, and temporal-association systems.
Conclusions:
Multiplex modeling of complementary connectome information provides signals for characterizing CKD-related neurocognitive brain-network dysfunction. MTGAF is a research-stage imaging-AI framework for individualized brain-risk characterization in dialysis-treated CKD and should be further evaluated in larger multi-center cohorts.
