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Key references relevant to EuroPOND
  • Fonteijn, et al. An event-based model for disease progression and its application in familial Alzheimer’s disease and Huntington’s disease. NeuroImage, 60(3):1880-1889, 2012.
  • Jedynak, et al. A computational neurodegenerative disease progression score: Method and results with the Alzheimer’s Disease Neuroimaging Initiative cohort. NeuroImage, 63(3):1478–1486, 2012.
  • Donohue, et al. Estimating long-term multivariate progression from short-term data. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 10(5):S400–S410, 2014.
  • Young, et al. A data-driven model of biomarker changes in sporadic Alzheimer’s disease. Brain, 137(9):2564-2577, 2014.
  • Young, et al. Data-driven models of neurodegenerative disease. Advances in Clinical Neuroscience and Rehabilitation 14(5):6, 2014.
  • Huang and Alexander. Probabilistic Event Cascades for Alzheimer’s disease. In Advances in Neural Information Processing Systems 25, pages 3104-3112. 2012.
  • Bilgel, et al. Temporal Trajectory and Progression Score Estimation from Voxelwise Longitudinal Imaging Measures: Application to Amyloid Imaging. In 24th biennial international conference on Information Processing in Medical Imaging (IPMI 2015), Lecture Notes in Computer Science. Springer, 2015.
  • Lorenzi, et al. Efficient Gaussian Process-Based Modelling and Prediction of Image Time Series. In 24th biennial international conference on Information Processing in Medical Imaging (IPMI 2015), Lecture Notes in Computer Science. Springer, 2015.
  • Lorenzi, et al. Regional flux analysis for discovering and quantifying anatomical changes: An application to the brain morphometry in Alzheimer’s disease. NeuroImage,  115 (15): 224–234, 2015.
  • Lorenzi, et al. Disentangling Normal Aging from Alzheimer’s Disease in Structural MR Images. Neurobiology of Aging, 36 Suppl 1:S42-52, 2015.
  • Schmidt-Richberg, et al. Multi-stage Biomarker Models for Progression Estimation in Alzheimer’s Disease. In 24th biennial international conference on Information Processing in Medical Imaging (IPMI 2015), Lecture Notes in Computer Science. Springer, 2015.
  • Schiratti, et al. A mixed-effect model with time reparametrization for longitudinal univariate manifold-valued data. In 24th biennial international conference on Information Processing in Medical Imaging (IPMI 2015), Lecture Notes in Computer Science. Springer, 2015.
  • Young, et al. Multiple Orderings of Events in Disease Progression. In 24th biennial international conference on Information Processing in Medical Imaging (IPMI 2015), Lecture Notes in Computer Science. Springer, 2015.
  • Young, et al. A simulation system for biomarker evolution in neurodegenerative disease. Medical Image Analysis 26(1):47-56, 2015.
  • Iturria-Medina, et al. Epidemic Spreading Model to Characterize Misfolded Proteins Propagation in Aging and Associated Neurodegenerative Disorders. PLoS Comput Biol, 10(11):e1003956, 11 2014.
  • Raj, et al. A Network Diffusion Model of Disease Progression in Dementia. Neuron, 73(6):1204-1215, 2012.
  • Zhou, et al. Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome. Neuron, 73(6):1216–1227, 2012; Alzheimer’s & Dementia, 10(4):159, 2014.
  • Villemagne, et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. The Lancet Neurology, 12(4):357 – 367, 2013.
  • Oxtoby, et al. Learning Imaging Biomarker Trajectories from Noisy Alzheimer’s Disease Data Using a Bayesian Multilevel Model. In Bayesian and grAphical Models for Biomedical Imaging, Lecture Notes in Computer Science, 8677:85–94, 2014.
  • Durrleman, et al. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision, 103(1):22-59, 2013.