{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(1)"],"submitter":["Rau A"],"pubmed_abstract":["To develop an imaging biomarker-based approach for the diagnosis of Post-COVID-condition (PCC) at the individual patient level. Magnetic resonance imaging (MRI) data from a prospective cohort of PCC patients (n = 89) were compared with a control group of unimpaired individuals who had contracted coronavirus disease 2019 (COVID-19) in the past (n = 38). Participants were divided into two groups: a training and a test cohort. The macrostructure, diffusion tensor imaging, and multi-shell-based microstructure imaging metrics were extracted using an atlas-based approach. These data were subsequently utilized to train a linear support vector machine (SVM). The efficacy of discrimination between the groups was evaluated for various combinations of input parameters. Upon comparison of the differen"],"journal":["Scientific reports"],"pagination":["32483"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12432129"],"repository":["biostudies-literature"],"pubmed_title":["Cerebral microstructural alterations as an imaging biomarker for Post-COVID-condition."],"pmcid":["PMC12432129"],"pubmed_authors":["Arnold PG","Hosp JA","Frase S","Urbach H","Reisert M","Rau A","Weiller C","Schroter N","Mast H"],"additional_accession":[]},"is_claimable":false,"name":"Cerebral microstructural alterations as an imaging biomarker for Post-COVID-condition.","description":"To develop an imaging biomarker-based approach for the diagnosis of Post-COVID-condition (PCC) at the individual patient level. Magnetic resonance imaging (MRI) data from a prospective cohort of PCC patients (n = 89) were compared with a control group of unimpaired individuals who had contracted coronavirus disease 2019 (COVID-19) in the past (n = 38). Participants were divided into two groups: a training and a test cohort. The macrostructure, diffusion tensor imaging, and multi-shell-based microstructure imaging metrics were extracted using an atlas-based approach. These data were subsequently utilized to train a linear support vector machine (SVM). The efficacy of discrimination between the groups was evaluated for various combinations of input parameters. Upon comparison of the differen","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-02T08:46:48.203Z","creation":"2026-04-16T03:13:02.794Z"},"accession":"S-EPMC12432129","cross_references":{"pubmed":["40940410"],"doi":["10.1038/s41598-025-18962-3"]}}