Random forest classifiers show myelin alterations in Long-COVID

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.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/4519
Primary Topic
Long-Term Effects of COVID-19
Type
article
Field-Weighted Citation Impact
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article

Random forest classifiers show myelin alterations in Long-COVID

Nicolò Rolandi, Janine Makaronidis, Fulvia Palesi, R. H. Samson et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Long-Term Effects of COVID-19
article

Random forest classifiers show myelin alterations in Long-COVID

Nicolò Rolandi, Janine Makaronidis, Fulvia Palesi, R. H. Samson, Carmen Tur, Michael S. Zandi, Elena Grosso, Jed Wingrove, Claudia A. M. Gandini Wheeler‐Kingshott, Francesco Grussu, Marios Yiannakas, Ferrán Prados, Madiha Shatila, Egidio D’Angelo, Olga Ciccarelli, Baris Kanber, Rachel L. Batterham, Marco Battiston, Antonio Ricciardi
article en

Abstract

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.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Reduced inequalities
Openalex Percentile: Top 67%
Long-Term Effects of COVID-19
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