Letter to the Editor
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Psychiatry. May 19, 2025; 15(5): 102953
Published online May 19, 2025. doi: 10.5498/wjp.v15.i5.102953
Unveiling the invisible: How cutting-edge neuroimaging transforms adolescent depression diagnosis
Haewon Byeon
Haewon Byeon, Worker's Care and Digital Health Lab, Department of Future Technology, Korea University of Technology and Education, Cheonan 31253, South Korea
Author contributions: Byeon H contributed to this paper and was involved in data interpretation and writing the article.
Supported by Basic Science Research Program through the National Research Foundation of Korea Funded by the Ministry of Education, No. NRF- RS-2023-00237287.
Conflict-of-interest statement: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.
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: Haewon Byeon, PhD, Associate Professor, Worker's Care and Digital Health Lab, Department of Future Technology, Korea University of Technology and Education, 1600 Chungjeol-Ro, Cheonan 31253, South Korea. bhwpuma@naver.com
Received: November 4, 2024
Revised: February 13, 2025
Accepted: February 24, 2025
Published online: May 19, 2025
Processing time: 178 Days and 5.1 Hours
Abstract

Yu et al's study has advanced the understanding of the neural mechanisms underlying major depressive disorder (MDD) in adolescents, emphasizing the significant role of the amygdala. While traditional diagnostic methods have limitations in objectivity and accuracy, this research demonstrates a notable advancement through the integration of machine learning techniques with neuroimaging data. Utilizing resting-state functional magnetic resonance imaging (fMRI), the study investigated functional connectivity (FC) in adolescents with MDD, identifying notable reductions in regions such as the left inferior temporal gyrus and right lingual gyrus, alongside increased connectivity in Vermis-10. The application of support vector machines (SVM) to resting-state fMRI (rs-fMRI) data achieved an accuracy of 83.91%, sensitivity of 79.55%, and specificity of 88.37%, with an area under the curve of 0.6765. These results demonstrate how SVM analysis of rs-fMRI data represents a significant improvement in diagnostic precision, with reduced FC in the right lingual gyrus emerging as a particularly critical marker. These findings underscore the critical role of the amygdala in MDD pathophysiology and highlight the potential of rs-fMRI and SVM as tools for identifying reliable neuroimaging biomarkers.

Keywords: Chronic multimorbidity; High complexity unit; Multidisciplinary approach; Patient-centered care; Integrated care model; Hospitalization metrics

Core Tip: Yu et al's research sheds light on adolescent major depressive disorder (MDD), focusing on the amygdala. The study explored neuroimaging biomarkers for diagnostics using resting-state functional magnetic resonance imaging to analyze functional connectivity (FC) in adolescents with MDD. It found reduced FC in the left inferior temporal gyrus and right lingual gyrus, alongside increased connectivity in Vermis-10. Support vector machines effectively distinguished MDD patients from healthy controls, highlighting reduced FC in the right lingual gyrus as a key marker. These findings suggest FC changes as reliable biomarkers, offering a non-invasive, accurate diagnostic approach for adolescent MDD.