Chaoqi Lv, Tao Liu, Huijuan Chen, Weiyuan Huang, Maochang Huang, Lan Zang, Chong Shen, Yihao Guo, Feng Chen
Development of two multimodal MRI fusion strategies using extended parallel multilink joint ICA combined with SVM and ensemble 3D ResNet model for pairwise binary classification among four groups: normal cognition (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD dementia (ADD). Optimal AUROC values for ADD vs. NC and SCD vs. NC were 95.68% and 81.25%, respectively. Systematic interpretability analyses identified model-associated imaging patterns consistent with prior AD-related findings and may provide insights into different disease stages.
Accurate identification of Alzheimer's disease (AD) stages is important for improving clinical understanding and supporting early-stage assessment. In this study, two multimodal MRI fusion strategies were developed to integrate structural MRI, resting-state functional MRI, and diffusion tensor imaging for pairwise binary classification among four groups: normal cognition (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and AD dementia (ADD). The extended parallel multilink joint independent component analysis (Epml-jICA) combined with a support vector machine (SVM) approach (machine learning) and the ensemble 3D ResNet model (deep learning) were evaluated on 664 participants with multimodal MRI. The results demonstrated that the optimal area under the receiver operating curve (AUROC) values for ADD vs. NC and SCD vs. NC were 95.68% and 81.25%, respectively. Furthermore, systematic interpretability analyses using SHAP, Grad-CAM, and anatomical localization of cross-modal important components were conducted, identifying model-associated imaging patterns that were consistent with prior AD-related findings and may provide insights into different disease stages.