Machine learning on longitudinal multi-modal data enables the understanding and prognosis of Alzheimer's disease progression

Alzheimer's disease (AD) is a complex pathophysiological disease. Allowing for heterogeneity, not only disease manifestations but also in different progression patterns, is critical for developing effective ease models that can be used in clinical and research settings. We introduce a machine learning 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). model shows potential for promoting the understanding of heterogeneous disease progression and predicting the conversion time to AD dementia.