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IEEE Trans. Neural Netw. Learn. Syst., May 21 2024, PP | https://doi.org/10.1109/TNNLS.2024.3394598
Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression
Seungwoo Jeong1, Wonsik Jung2, Junghyo Sohn1, and Heung-Il Suk3, Senior Member, IEEE
1Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea
2Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
3Department of Artificial Intelligence and the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
Seungwoo Jeong and Wonsik Jung contributed equally to this work.
Corresponding author: Heung-Il Suk.
Abstract
Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progression over time is vital for clinical diagnosis and treatment. For this, numerous studies have implemented structural magnetic resonance imaging (MRI) to model AD progression, focusing on three integral aspects: 1) temporal variability; 2) incomplete observations; and 3) temporal geometric characteristics. However, many pioneer deep learning-based approaches addressing data variability and sparsity have yet to consider inherent geometrical properties sufficiently. These properties are integral to modeling as they correlate with brain region size, thickness, volume, and shape in AD progression. The ordinary differential equation-based geometric modeling method (ODE-RGRU) has recently emerged as a promising strategy for modeling time-series data by intertwining a recurrent neural network (RNN) and an ODE in Riemannian space. Despite its achievements, ODE-RGRU encounters limitations when extrapolating positive definite symmetric matrices from incomplete samples, leading to feature reverse occurrences that are particularly problematic, especially within the clinical facet. Therefore, this study proposes a novel geometric learning approach that models longitudinal MRI biomarkers and cognitive scores by combining three modules: topological space shift, ODE-RGRU, and trajectory estimation. We have also developed a training algorithm that integrates the manifold mapping with monotonicity constraints to reflect measurement transition irreversibility. We verify our proposed method's efficacy by predicting clinical labels and cognitive scores over time in regular and irregular settings. Furthermore, we thoroughly analyze our proposed framework through an ablation study.
논문정보
- Research article
- 2024년 05월 (BRIC 등록일 2024-05-22)
- 국내 연구진
- 바이오·의료융합 > 뇌인지과학
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