Summary: This project developed new computer science technology for modelling the progression of a disease or developmental process. It pioneers the use of state-of-the-art generative modelling and learning techniques to address this problem.
Young, A.L. et al (2015). A simulation system for biomarker evolution in neurodegenerative disease, MedIA 26, pp 47-56.
Young, A.L. et al (2015). Multiple Orderings of Events in Disease Progression, LNCS 9123, pp 711-722. (IPMI 2015)
Young, A.L. et al (2014). A data-driven model of biomarker changes in sporadic Alzheimer’s disease, Brain 137, pp 2564-2577.
Oxtoby, N.P., et al (2014). Learning Imaging Biomarker Trajectories from Noisy Alzheimer’s Disease Data Using a Bayesian Multilevel Model, LNCS 8677, p 85. (Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014)
Huang, J. and Alexander, D. C. (2012). Probabilistic Event Cascades for Alzheimer’s disease, Advances in Neural Information Processing Systems 25, pp 3095-3103. (Neural Information Processing Symposium, NIPS 2012)
Fonteijn, H. et al (2012). An event-based model for disease progression and its application in familial Alzheimer’s disease and Huntington’s disease, NeuroImage 60(3), pp 1880–1889.
Fonteijn, H. et al (2011). An Event-Based Disease Progression model and its application to familial Alzheimer’s Disease, LNCS 6801, pp 748-759. (Information Processing in Medical Imaging, IPMI 2011)
Summary: This project investigated the transneuronal hypothesis of disease propagation in Parkinson’s disease and Alzheimer’s disease. Our approach utilised modelling ideas from the NeuroProgression and EuroPOND projects.
Firth, et al. (2020), Sequences of cognitive decline in typical Alzheimer’s disease and posterior cortical atrophy estimated using a novel event-based model of disease progression, Alzheimer’s & Dementia.
Oxtoby, et al. (2017), Data Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease, Frontiers in Neurology.
C-PLACID: Computational PLatform for Assessment of Cognition in Dementia
Summary: This project aims to develop new cognitive tests for early detection and accurate subtyping of dementia using traditional question and answer tests as well as new virtual reality scenario testing. The work draws on the ideas of disease progression modelling coming out of the NeuroProgression project.
Summary: Raz’s PhD project investigated the progression of neuroimaging measures in typical Alzheimer’s disease and Posterior Cortical Atrophy. The aim is to develop specialised mathematical models that allow us to compare the temporal heterogeneity of different Alzheimer’s disease variants.
Marinescu, R.V. et al. (2017). A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images, IPMI 2017.
Marinescu, R.V. et al. (2019). DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders. NeuroImage 192, 166-177.