Abstract #1795
Developing pattern recognition models to extract longitudinal network-based measures at an individual level
Elisa Colato1, Claudia AM Wheeler-Kingshott1,2,3, Douglas L Arnold4, Frederik Barkhof1,5,6,7, Olga Ciccarelli1,5, Declan Chard1,5, and Arman Eshaghi1,8
1Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, United Kingdom, 2Brain MRI 3T Research Centre, C. Mondino National Neurological Institute, Pavia, Italy, 3Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, 4McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada, 5Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, United Kingdom, 6Department of Radiology and Nuclear Medici, VU medical centre, Amsterdam, Netherlands, 7Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, United Kingdom, 8Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London (UCL), London, United Kingdom
Synopsis
Network-based measures can outperform regional and whole-brain grey matter (GM) measures in explaining clinical disability in several neurodegenerative disorders. However, network measures are mostly estimated at the group level and require a re-estimation of model parameters when applied to new participants. Here, we introduce a new longitudinal network analysis paradigm to extract longitudinal ICA-like components at an individual level from a discovery cohort, applied machine learning to obtain individual-level network-based measure for a validation cohort, and used them to explain clinical disability in multiple sclerosis.
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