Predict Disease Progression With Reaction Rate Equation Modeling of Multimodal MRI and PET

Neurodegenerative dementia often has multiple types of underlying pathology, for example, beta-amyloid, misfolded tau, chronic neuroinflammation and neurodegeneration may coexist in Alzheimer’s disease. However, the relationship between them is often unclear, in other words, whether one pathology is upstream or downstream of others can be very difficult to investigate directly. This is partly because the underlying pathology in dementia may precede detectable symptoms by several years if not decades. The time scale associated with disease progression in dementia generally exceeds that in conventional longitudinal imaging studies in humans, so it is difficult to directly observe the temporal ordering of different pathologies. Also, animal studies are not always transferable to patients due to obvious differences between the two systems. To investigate the disease progression and causal relationships among underlying pathological changes, we propose a novel computational modelling approach for multimodal MRI and PET inspired by reaction rate equation in chemical kinetics. Although longitudinal data provides stronger evidence, the method is applicable to cross-sectional data as it has been shown that the rate of change in biomarkers can often be approximated by the average trajectory across patients at different stages of disease severity in the cross-sectional studies. The relationship modelling in our approach is akin to that in the control theory, and can be assessed by demonstrating that the presence of one disease related biomarker predicts dynamics in another. We argue that modelling disease progression and the relationship among multiple imaging biomarkers has important implications for trials targeting different pathologies in dementia.