Brain network connectivity and classification model of adolescent depression based on resting-state functional magnetic resonance imaging and machine learning
10.3760/cma.j.cn115354-20241124-00736
- VernacularTitle:基于静息态功能磁共振成像和机器学习的青少年抑郁症脑网络连接及分类模型研究
- Author:
Yanrui SHEN
1
;
Xuekun LI
;
Zhong LI
;
Chenghao CAO
;
Zhuo ZHENG
;
Baolin WU
Author Information
1. 四川大学华西医院放射科，成都 610041
- Publication Type:Journal Article
- Keywords:
Depression;
Adolescent;
Independent component analysis;
Functional connectivity;
Resting-state functional magnetic resonance imaging;
Machine learning
- From:
Chinese Journal of Neuromedicine
2025;24(3):260-266
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the abnormal patterns of brain functional network connectivity in depression adolescents and their diagnostic value in adolescent depression.Methods:A total of 94 depression adolescents (adolescent depression group) admitted to Outpatient Department of Psychiatric Imaging, West China Hospital, Sichuan University from January 2020 to December 2022 were selected. In addition, 78 age- and gender-matched healthy adolescents were recruited from local community advertisements at the same time-period as healthy control group. Resting-state functional magnetic resonance imaging was performed; after image preprocessing, group-level spatial independent component analysis was performed to identify the intrinsic network connectivity, and differences in network connectivity between the two groups were compared. Functional connectivity edges were employed as classification features, and feature ranking and screening were then performed. A support vector machine (SVM) with linear kernel function was used to construct a classification model, and receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of this classification model in adolescent depression.Results:No significant difference was noted in age, gender, years of education, and body mass index between the two groups ( P>0.05). Compared with the healthy control group, the adolescent depression group had significantly decreased functional connectivity intensity within and between the networks of sensorimotor network (SMN), visual network (VN), auditory network (AN), default mode network (DMN), and cognitive control network (CCN), and significantly increased functional connectivity intensity within CCN ( P<0.05). When using the 75 top-ranked functional connectivity features, this classification model had the best performance (accuracy rate: 70.35%, sensitivity: 70.21%, specificity: 71.80%, P<0.001). ROC curve showed that area under the curve of this classification model in diagnosing adolescent depression was 0.724 (95% CI: 0.648-0.800, P<0.001). A total of 51 consistent functional connectivities were identified and they were mainly located within or between the networks of SMN, VN, AN, DMN, and CCN. Conclusion:The abnormal resting-state brain functional connectivity in depression adolescents can provide imaging basis for their clinical diagnosis.
