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Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features

DC Field Value Language
dc.contributor.author김민경-
dc.contributor.author김재진-
dc.contributor.author김병훈-
dc.date.accessioned2022-12-22T03:30:38Z-
dc.date.available2022-12-22T03:30:38Z-
dc.date.issued2022-08-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/191895-
dc.description.abstractSocial anxiety is a symptom widely prevalent among young adults, and when present in excess, can lead to maladaptive patterns of social behavior. Recent approaches that incorporate brain functional radiomic features and machine learning have shown potential for predicting certain phenotypes or disorders from functional magnetic resonance images. In this study, we aimed to predict the level of social anxiety in young adult participants by training machine learning models with resting-state brain functional radiomic features including the regional homogeneity, fractional amplitude of low-frequency fluctuation, fractional resting-state physiological fluctuation amplitude, and degree centrality. Among the machine learning models, the XGBoost model achieved the best performance with balanced accuracy of 77.7% and F1 score of 0.815. Analysis of input feature importance demonstrated that the orbitofrontal cortex and the degree centrality were most relevant to predicting the level of social anxiety among the input brain regions and the input type of radiomic features, respectively. These results suggest potential validity for predicting social anxiety with machine learning of the resting-state brain functional radiomic features and provide further understanding of the neural basis of the symptom.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAnxiety / diagnostic imaging-
dc.subject.MESHBrain Mapping / methods-
dc.subject.MESHBrain* / diagnostic imaging-
dc.subject.MESHHumans-
dc.subject.MESHMachine Learning*-
dc.subject.MESHMagnetic Resonance Imaging / methods-
dc.titlePredicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentOthers-
dc.contributor.googleauthorByung-Hoon Kim-
dc.contributor.googleauthorMin-Kyeong Kim-
dc.contributor.googleauthorHye-Jeong Jo-
dc.contributor.googleauthorJae-Jin Kim-
dc.identifier.doi10.1038/s41598-022-17769-w-
dc.contributor.localIdA04894-
dc.contributor.localIdA00870-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid35977968-
dc.contributor.alternativeNameKim, Min Kyung-
dc.contributor.affiliatedAuthor김민경-
dc.contributor.affiliatedAuthor김재진-
dc.citation.volume12-
dc.citation.number1-
dc.citation.startPage13932-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.12(1) : 13932, 2022-08-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Psychiatry (정신과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Others (기타) > 1. Journal Papers

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