Yu ZH, Yu RQ, Wang XY, Ren WY, Zhang XQ, Wu W, Li X, Dai LQ, Lv YL. Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents. World J Psychiatry 2024; 14(11): 1696-1707 [DOI: 10.5498/wjp.v14.i11.1696]
Corresponding Author of This Article
Ren-Qiang Yu, PhD, Doctor, Lecturer, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China. yurenqiang@hospital.cqmu.edu.cn
Research Domain of This Article
Clinical Neurology
Article-Type of This Article
Observational Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Zhi-Hui Yu, Ren-Qiang Yu, Xing-Yu Wang, Wen-Yu Ren, Xiao-Qin Zhang, Wei Wu, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
Xiao Li, Lin-Qi Dai, Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
Ya-Lan Lv, School of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
Author contributions: Yu ZH performed the literature search, collected the data and drafted and approved the final manuscript; Yu RQ conceived the idea and designed the study; Wang XY and Ren WY conducted magnetic resonance imaging scans on the subjects and approved the final manuscript; Yu RQ, Zhang XQ and Wu W reviewed the draft and approved the final manuscript; Li X and Dai LQ completed the clinical scale assessments of the subjects and approved the final manuscript; Lv YL analyzed the data and approved the final manuscript; All the authors contributed to this manuscript.
Institutional review board statement: This study was reviewed and approved by the local ethical review board (The First Affiliated Hospital of Chongqing Medical University, No. 20214801).
Informed consent statement: All the individuals who participated in this study provided their written informed consent prior to study enrolment.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ren-Qiang Yu, PhD, Doctor, Lecturer, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China. yurenqiang@hospital.cqmu.edu.cn
Received: May 26, 2024 Revised: October 9, 2024 Accepted: October 30, 2024 Published online: November 19, 2024 Processing time: 165 Days and 4 Hours
Abstract
BACKGROUND
Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder (MDD). However, few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity (FC).
AIM
To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.
METHODS
Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study. Using resting-state functional magnetic resonance imaging, the FC was compared between the adolescents with MDD and the healthy controls, with the bilateral amygdala serving as the seed point, followed by statistical analysis of the results. The support vector machine (SVM) method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.
RESULTS
Compared to the controls and using the bilateral amygdala as the region of interest, patients with MDD showed significantly lower FC values in the left inferior temporal gyrus, bilateral calcarine, right lingual gyrus, and left superior occipital gyrus. However, there was an increase in the FC value in Vermis-10. The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls, achieving a diagnostic accuracy of 83.91%, sensitivity of 79.55%, specificity of 88.37%, and an area under the curve of 67.65%.
CONCLUSION
The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.
Core Tip: We want to explore the potential neuroimaging biomarkers of adolescents with major depressive disorder using resting-state functional magnetic resonance imaging and support vector machines. The results showed that using the abnormal functional connectivity value of the right linguistic gyrus as a biomarker to distinguish patients and healthy controls had certain advantages, which was of great significance for the early diagnosis and treatment of adolescent major depressive disorder.
