Cargando…
Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features
OBJECTIVE: Headache is among the most frequent symptoms after coronavirus disease 2019 (COVID-19), so-called long COVID syndrome. Although distinct brain changes have been reported in patients with long COVID, such reported brain changes have not been used for predictions and interpretations in a mu...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267340/ https://www.ncbi.nlm.nih.gov/pubmed/37323930 http://dx.doi.org/10.3389/fnhum.2023.1202103 |
_version_ | 1785058903878270976 |
---|---|
author | Kim, Minhoe Sim, Sunkyung Yang, Jaeseok Kim, Minchul |
author_facet | Kim, Minhoe Sim, Sunkyung Yang, Jaeseok Kim, Minchul |
author_sort | Kim, Minhoe |
collection | PubMed |
description | OBJECTIVE: Headache is among the most frequent symptoms after coronavirus disease 2019 (COVID-19), so-called long COVID syndrome. Although distinct brain changes have been reported in patients with long COVID, such reported brain changes have not been used for predictions and interpretations in a multivariate manner. In this study, we applied machine learning to assess whether individual adolescents with long COVID can be accurately distinguished from those with primary headaches. METHODS: Twenty-three adolescents with long COVID headaches with the persistence of headache for at least 3 months and 23 age- and sex-matched adolescents with primary headaches (migraine, new daily persistent headache, and tension-type headache) were enrolled. Multivoxel pattern analysis (MVPA) was applied for disorder-specific predictions of headache etiology based on individual brain structural MRI. In addition, connectome-based predictive modeling (CPM) was also performed using a structural covariance network. RESULTS: MVPA correctly classified long COVID patients from primary headache patients, with an area under the curve of 0.73 (accuracy = 63.4%; permutation p = 0.001). The discriminating GM patterns exhibited lower classification weights for long COVID in the orbitofrontal and medial temporal lobes. The CPM using the structural covariance network achieved an area under the curve of 0.81 (accuracy = 69.5%; permutation p = 0.005). The edges that classified long COVID patients from primary headache were mainly comprising thalamic connections. CONCLUSION: The results suggest the potential value of structural MRI-based features for classifying long COVID headaches from primary headaches. The identified features suggest that the distinct gray matter changes in the orbitofrontal and medial temporal lobes occurring after COVID, as well as altered thalamic connectivity, are predictive of headache etiology. |
format | Online Article Text |
id | pubmed-10267340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102673402023-06-15 Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features Kim, Minhoe Sim, Sunkyung Yang, Jaeseok Kim, Minchul Front Hum Neurosci Neuroscience OBJECTIVE: Headache is among the most frequent symptoms after coronavirus disease 2019 (COVID-19), so-called long COVID syndrome. Although distinct brain changes have been reported in patients with long COVID, such reported brain changes have not been used for predictions and interpretations in a multivariate manner. In this study, we applied machine learning to assess whether individual adolescents with long COVID can be accurately distinguished from those with primary headaches. METHODS: Twenty-three adolescents with long COVID headaches with the persistence of headache for at least 3 months and 23 age- and sex-matched adolescents with primary headaches (migraine, new daily persistent headache, and tension-type headache) were enrolled. Multivoxel pattern analysis (MVPA) was applied for disorder-specific predictions of headache etiology based on individual brain structural MRI. In addition, connectome-based predictive modeling (CPM) was also performed using a structural covariance network. RESULTS: MVPA correctly classified long COVID patients from primary headache patients, with an area under the curve of 0.73 (accuracy = 63.4%; permutation p = 0.001). The discriminating GM patterns exhibited lower classification weights for long COVID in the orbitofrontal and medial temporal lobes. The CPM using the structural covariance network achieved an area under the curve of 0.81 (accuracy = 69.5%; permutation p = 0.005). The edges that classified long COVID patients from primary headache were mainly comprising thalamic connections. CONCLUSION: The results suggest the potential value of structural MRI-based features for classifying long COVID headaches from primary headaches. The identified features suggest that the distinct gray matter changes in the orbitofrontal and medial temporal lobes occurring after COVID, as well as altered thalamic connectivity, are predictive of headache etiology. Frontiers Media S.A. 2023-06-01 /pmc/articles/PMC10267340/ /pubmed/37323930 http://dx.doi.org/10.3389/fnhum.2023.1202103 Text en Copyright © 2023 Kim, Sim, Yang and Kim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Kim, Minhoe Sim, Sunkyung Yang, Jaeseok Kim, Minchul Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features |
title | Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features |
title_full | Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features |
title_fullStr | Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features |
title_full_unstemmed | Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features |
title_short | Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features |
title_sort | multivariate prediction of long covid headache in adolescents using gray matter structural mri features |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267340/ https://www.ncbi.nlm.nih.gov/pubmed/37323930 http://dx.doi.org/10.3389/fnhum.2023.1202103 |
work_keys_str_mv | AT kimminhoe multivariatepredictionoflongcovidheadacheinadolescentsusinggraymatterstructuralmrifeatures AT simsunkyung multivariatepredictionoflongcovidheadacheinadolescentsusinggraymatterstructuralmrifeatures AT yangjaeseok multivariatepredictionoflongcovidheadacheinadolescentsusinggraymatterstructuralmrifeatures AT kimminchul multivariatepredictionoflongcovidheadacheinadolescentsusinggraymatterstructuralmrifeatures |