TITLE:
CASCADE-Net: Causality-Aware Spatio-Temporal Dynamics Encoding for Prognostic Prediction in Mild Cognitive Impairment
AUTHORS:
Samuel Ocen, Lawrence Muchemi, Michaelina Almaz Yohannis
KEYWORDS:
Alzheimer’s Disease, Mild Cognitive Impairment, Prognosis, Neural ODE, Counterfactual Learning, Spatio-Temporal Modeling, Interpretable AI
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.17 No.4,
October
15,
2025
ABSTRACT: Predicting the progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) is a critical challenge for enabling early intervention and improving patient outcomes. While longitudinal multi-modal neuroimaging data holds immense potential for capturing the spatio-temporal dynamics of disease progression, its effective analysis is hampered by significant challenges: temporal heterogeneity (irregularly sampled scans), multi-modal misalignment, and the propensity of deep learning models to learn spurious, non-causal correlations. We propose CASCADE-Net, a novel end-to-end pipeline for robust and interpretable MCI-to-AD progression prediction. Our architecture introduces a Dynamic Temporal Alignment Module that employs a Neural Ordinary Differential Equation (Neural ODE) to model the continuous, underlying progression of pathology from irregularly sampled scans, effectively mapping heterogeneous patient data to a unified latent timeline. This aligned, noise-reduced spatio-temporal data is then processed by a predictive model featuring a novel Causal Spatial Attention mechanism. This mechanism not only identifies the critical brain regions and their evolution predictive of conversion but also incorporates a counterfactual constraint during training. This constraint ensures the learned features are causally linked to AD pathology by encouraging invariance to non-causal, confounder-based changes. Extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that CASCADE-Net significantly outperforms state-of-the-art sequential models in prognostic accuracy. Furthermore, our model provides highly interpretable, causally-grounded attention maps, offering valuable insights into the disease progression process and fostering greater clinical trust.