Machine learning on longitudinal multi-modal data enables the understanding and prognosis of Alzheimer’s disease progression
Suixia Zhang,
Jing Yuan,
Yu Sun,
Fei Wu,
Ziyue Liu,
Feifei Zhai,
Yaoyun Zhang,
Judith Somekh,
Mor Peleg,
Yi-Cheng Zhu,
Zhengxing Huang
Affiliations
Suixia Zhang
Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China; Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
Jing Yuan
Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
Yu Sun
Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
Fei Wu
Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
Ziyue Liu
Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
Feifei Zhai
Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
Department of Information Systems, University of Haifa, Haifa 3303220, Israel
Mor Peleg
Department of Information Systems, University of Haifa, Haifa 3303220, Israel
Yi-Cheng Zhu
Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China; Corresponding author
Summary: Alzheimer’s disease (AD) is a complex pathophysiological disease. Allowing for heterogeneity, not only in disease manifestations but also in different progression patterns, is critical for developing effective disease models that can be used in clinical and research settings. We introduce a machine learning model for identifying underlying patterns in Alzheimer’s disease (AD) trajectory using longitudinal multi-modal data from the ADNI cohort and the AIBL cohort. Ten biologically and clinically meaningful disease-related states were identified from data, which constitute three non-overlapping stages (i.e., neocortical atrophy [NCA], medial temporal atrophy [MTA], and whole brain atrophy [WBA]) and two distinct disease progression patterns (i.e., NCA → WBA and MTA → WBA). The index of disease-related states provided a remarkable performance in predicting the time to conversion to AD dementia (C-Index: 0.923 ± 0.007). Our model shows potential for promoting the understanding of heterogeneous disease progression and early predicting the conversion time to AD dementia.
