Abstract #4519
Random forest classifiers show myelin alterations in Long-COVID
Antonio Ricciardi1, Elena Grosso2, Madiha Shatila1, Michael S. Zandi3, Marios C. Yiannakas1, Ferran Prados1,4,5, Baris Kanber1,4, Jed Wingrove1, Francesco Grussu1,6, Marco Battiston1, Rebecca S. Samson1, Carmen Tur7, Rachel L. Batterham8,9, Janine Makaronidis8,9, Nicolò Rolandi1,2,10, Fulvia Palesi2,11, Egidio D'Angelo2,11, Olga Ciccarelli1,9, and Claudia A. M. Gandini Wheeler-Kingshott1,2,11
1NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 2Department of Brain & Behavioural Sciences, University of Pavia, Pavia, Italy, 3Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 4Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom, 5E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain, 6Radiomics Group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, 7Neurology-Neuroimmunology Department Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, 8Centre for Obesity Research, Department of Medicine, University College London, London, United Kingdom, 9National Institute of Health Research, Biomedical Research Centre at UCLH and UCL, University College London, London, United Kingdom, 10Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 11Digital Neuroscience Center, IRCCS Mondino Foundation, Pavia, Italy
Synopsis
Keywords: Analysis/Processing, COVID-19, Long-COVID, Multimodal, MRI, myelin.
Motivation: Some people exposed to SARS-CoV-2 experience persistent symptoms months after recovering from acute infection. These symptoms, grouped under the umbrella of “long-COVID”, include neurological and autonomic dysfunctions for which there is not yet a clear explanation.
Goal(s): To identify biophysically meaningful features characterising long-COVID pathophysiology in the brain.
Approach: A novel multimodal MRI protocol was developed to measure iron accumulation, myelin content, inflammation, microstructure alterations, atrophy, and cerebral blood flow changes. Machine learning was employed to discriminate long-COVID from controls and people who fully recovered.
Results: Alterations in bound-pool fraction (myelin content) emerge in long-COVID.
Impact: This study encourages investigating the neurological aspect of long-COVID and its interplay with conditions affecting myelin as it shows that long-COVID may cause myelin alterations or be more likely to occur in people with compromised myelin content in the brain.
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