Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Université Côte d’Azur, Inria, Epione Research Project, Sophia Antipolis, France
Marco Lorenzi
Université Côte d’Azur, Inria, Epione Research Project, Sophia Antipolis, France
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
Elisabeth J Vinke
Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
Razvan V Marinescu
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
Arman Eshaghi
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
M Arfan Ikram
Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands; Department of Radiology and Nuclear medicine, Erasmus MC, Rotterdam, Netherlands
Wiro J Niessen
Department of Radiology and Nuclear medicine, Erasmus MC, Rotterdam, Netherlands
Olga Ciccarelli
Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Department of Radiology and Nuclear medicine, VUmc, Amsterdam, Netherlands
Jonathan M Schott
Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom
Meike W Vernooij
Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands; Department of Radiology and Nuclear medicine, Erasmus MC, Rotterdam, Netherlands
Daniel C Alexander
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
for the Alzheimer’s Disease Neuroimaging Initiative
The spatial distribution of atrophy in neurodegenerative diseases suggests that brain connectivity mediates disease propagation. Different descriptors of the connectivity graph potentially relate to different underlying mechanisms of propagation. Previous approaches for evaluating the influence of connectivity on neurodegeneration consider each descriptor in isolation and match predictions against late-stage atrophy patterns. We introduce the notion of a topological profile — a characteristic combination of topological descriptors that best describes the propagation of pathology in a particular disease. By drawing on recent advances in disease progression modeling, we estimate topological profiles from the full course of pathology accumulation, at both cohort and individual levels. Experimental results comparing topological profiles for Alzheimer’s disease, multiple sclerosis and normal ageing show that topological profiles explain the observed data better than single descriptors. Within each condition, most individual profiles cluster around the cohort-level profile, and individuals whose profiles align more closely with other cohort-level profiles show features of that cohort. The cohort-level profiles suggest new insights into the biological mechanisms underlying pathology propagation in each disease.