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

 Byung-Hoon Kim  ;  Min-Kyeong Kim  ;  Hye-Jeong Jo  ;  Jae-Jin Kim 
 SCIENTIFIC REPORTS, Vol.12(1) : 13932, 2022-08 
Journal Title
Issue Date
Anxiety / diagnostic imaging ; Brain Mapping / methods ; Brain* / diagnostic imaging ; Humans ; Machine Learning* ; Magnetic Resonance Imaging / methods
Social 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.
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1. College of Medicine (의과대학) > Dept. of Psychiatry (정신과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Others (기타) > 1. Journal Papers
Yonsei Authors
Kim, Min Kyeong(김민경) ORCID logo https://orcid.org/0000-0001-6044-9422
Kim, Byung Hoon(김병훈)
Kim, Jae Jin(김재진) ORCID logo https://orcid.org/0000-0002-1395-4562
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