Temporal‐multimodal consistency alignment for Alzheimer's cognitive assessment prediction

作者：Xikai Yang, Xilin Dang, Jinyue Cai, Jinpeng Li, Xi Wang, Pheng‐Ann Heng · 发表于：Medical Physics · 年份：2025 · DOI：10.1002/mp.17767 · 被引用次数：4 · 研究领域：Dementia and Cognitive Impairment Research、Machine Learning in Healthcare、Functional Brain Connectivity Studies

BACKGROUND: As one of the most prevalent neurodegenerative disorders, Alzheimer's disease (AD) severely impacts human thinking and behavior. Early and accurate prediction of cognitive decline is crucial for timely AD intervention. However, most existing prognostic methods hardly explore the underlying association among longitudinal data from different modalities in disease progression, thus the predictive ability of current models is still quite limited. PURPOSE: We propose the unifying Multi-Modality fusion with DUal-gRanularity Alignment framework (MM-DURA) to simultaneously model longitudinal correlations and modalities interactions for cognitive assessment forecasting. Our proposed framework leverages temporal MRI scans, time-aligned clinical diagnostics, and genomic data as inputs to forecast multiple cognitive assessment scores. METHODS: We propose a novel coarse-to-fine feature representation learning approach to ascertain the congruence between modalities at both the subject and visit granularities. This method ensures the alignment of multimodal data pertaining to individual subjects and captures the temporal progression of these modalities. Additionally, we design a hierarchical multimodality fusion (HMF) block that can effectively exploit the interrelationships and dependencies among modalities. Lastly, we employ an LSTM-based regression head with the fused multimodality embedding as input to forecast the future status of cognitive ability. RESULTS: We validate our...