MAGNET-AD: Multitask Spatiotemporal GNN for Interpretable Prediction of PACC and Conversion Time in Preclinical Alzheimer
Hassan, Salma Salem, Mostafa Papineni, Vijay Ram Kumar Elsayed, Ayman Yaqub, Mohammad
Hassan, Salma
Salem, Mostafa
Papineni, Vijay Ram Kumar
Elsayed, Ayman
Yaqub, Mohammad
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Preclinical Alzheimer’s Disease (AD) detection remains challenging due to the complex interplay of biological, structural, and temporal factors. Existing methods often struggle to integrate multimodal longitudinal data and predict key clinical outcomes. We propose MAGNET-AD, a novel multitask spatiotemporal graph neural network designed to predict the Preclinical Alzheimer’s Cognitive Composite (PACC) score and time to AD conversion. MAGNET-AD offers three key contributions: (1) A dynamic heterogeneous graph architecture with weighted edges for hybrid fusion mechanisms, integrating static and dynamic multimodal data; (2) a temporal importance weighting loss function that adaptively learns critical time points while jointly optimizing time prediction and cognitive decline estimation; and (3) an interpretable attention framework that highlights key brain regions and genetic factors driving disease progression. MAGNET-AD achieves state-of-the-art performance with a concordance index of 0.858 for conversion time prediction and a mean square error of 1.983 for PACC prediction, outperforming existing deep learning approaches. These results underscore MAGNET-AD’s potential for early AD risk assessment and monitoring, enabling broader clinical applications. The code is available at https://github.com/BioMedIA-MBZUAI/MAGNET-AD
Citation
S. Hassan, M. Salem, V. R. K. Papineni, A. Elsayed, and M. Yaqub, “MAGNET-AD: Multitask Spatiotemporal GNN for Interpretable Prediction of PACC and Conversion Time in Preclinical Alzheimer,” pp. 348–357, 2026, doi: 10.1007/978-3-032-05185-1_34
Source
Medical Image Computing and Computer Assisted Intervention
Conference
28 MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Keywords
Spatiotemporal Graph Neural Network, Multi-Task Learning, Dementia, Neuroimaging, Multimodal, Interpretability
Subjects
Source
28 MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Publisher
Springer Nature