Jingwen Zhang
ABSTRACT
Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder, where beta-amyloid (A), pathologic tau (T), neurodegeneration ([N]), and structural brain network (Net) are four major indicators of AD progression. Most current studies on AD rely on single-source modality and ignore complex biological interactions at molecular level. In this study, we propose a novel multimodal spatiotemporal stratification network (MSSN) that is built upon the fusion of multiple data modalities and the combined power of systems biology and deep learning. Altogether, our stratification approach could (1) ameliorate limitations caused by insufficient longitudinal imaging data, (2) extract important spatiotemporal features vectors from imaging data, (3) exploit the subject-specific longitudinal prediction of a holistic biomarker set, and (4) generate symptoms related finegrained subtype classification.
- C. R. Jack et al., "Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade," Lancet Neurol., vol. 9, no. 1, pp. 119--128, Jan. 2010 Google Scholar
Cross Ref
- C. R. Jack and D. M. Holtzman, "Biomarker modeling of Alzheimer's disease," Neuron, vol. 80, no. 6, pp. 1347--1358, Dec. 2013 Google ScholarCross Ref
- C. Lee and M. V. D. Schaar, "Temporal Phenotyping using Deep Predictive Clustering of Disease Progression," in Proceedings of the 37th International Conference on Machine Learning, Nov. 2020, pp. 5767--5777.Google Scholar
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