Li WT, Wan YM, Miao W, Zhong R, Gao Q, Wang QX, Zheng YS. Innovations and approaches in depression detection via functional near-infrared spectroscopy. World J Psychiatry 2026; 16(3): 112056 [DOI: 10.5498/wjp.v16.i3.112056]
Corresponding Author of This Article
Qing-Xiang Wang, Shandong Mental Health Center, Shandong University, No. 49 Wenhua East Road, Jinan 250014, Shandong Province, China. wangqx@qlu.edu.cn
Research Domain of This Article
Medical Informatics
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Minireviews
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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/
Mar 19, 2026 (publication date) through Feb 27, 2026
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Journal Information of This Article
Publication Name
World Journal of Psychiatry
ISSN
2220-3206
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Li WT, Wan YM, Miao W, Zhong R, Gao Q, Wang QX, Zheng YS. Innovations and approaches in depression detection via functional near-infrared spectroscopy. World J Psychiatry 2026; 16(3): 112056 [DOI: 10.5498/wjp.v16.i3.112056]
Wen-Tao Li, Yu-Mei Wan, Wei Miao, Rui Zhong, Qing-Xiang Wang, Yun-Shao Zheng, Shandong Mental Health Center, Shandong University, Jinan 250014, Shandong Province, China
Qian Gao, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250300, Shandong Province, China
Co-first authors: Wen-Tao Li and Yu-Mei Wan.
Co-corresponding authors: Qing-Xiang Wang and Yun-Shao Zheng.
Author contributions: Li WT and Wan YM contributed equally to this manuscript and are co-first authors. Wang QX and Zheng YS contributed equally to this manuscript and are co-corresponding authors. Li WT, Wan YM, Wang QX, and Zheng YS designed the research study; Zhong R, Miao W, and Gao Q conducted literature retrieval; Li WT and Wan YM summarized and analyzed relevant literature; Zheng YS, Wan YM, and Miao W provided medical knowledge; Li WT and Wan YM were responsible for writing and revising the manuscript; Wang QX and Zheng YS reviewed the manuscript and approved its publication. All authors have read and approved the final manuscript.
Supported by Shandong Provincial Medical and Health Science and Technology, No. 202303090824; Science and Technology Development Plan of Jinan (Clinical Medicine Science and Technology Innovation Plan), No. 202225054; Shandong Provincial Medical and Health Science and Technology, No. 202203090935; and Shandong Provincial Natural Science Foundation, No. ZR2022MF333.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Qing-Xiang Wang, Shandong Mental Health Center, Shandong University, No. 49 Wenhua East Road, Jinan 250014, Shandong Province, China. wangqx@qlu.edu.cn
Received: July 17, 2025 Revised: August 27, 2025 Accepted: December 10, 2025 Published online: March 19, 2026 Processing time: 225 Days and 15.5 Hours
Abstract
Depression, a leading contributor to global disability, lacks objective diagnostic biomarkers. This review evaluates functional near-infrared spectroscopy (fNIRS) as a portable neuroimaging tool for depression detection, highlighting its algorithmic innovations and clinical translation potential. Machine learning techniques effectively decode hemodynamic patterns of the prefrontal cortex during emotional or cognitive tasks to achieve high classification accuracy in controlled studies. Clinically, fNIRS identifies prefrontal cortex hypoactivation as correlated with symptom severity and tracks neuroplasticity during psychotherapy. However, heterogeneity across symptom subtypes, cultural backgrounds, and age groups limits the generalizability of the model. Technical challenges include signal noise from motion artifacts and interference from superficial tissues. Future research should prioritize standardized multicenter trials, multimodal integration to enhance biomarker specificity, and interpretable artificial intelligence frameworks for clinical translation. fNIRS demonstrates unique advantages for scalable, noninvasive depression screening but necessitates rigorous validation to transition from research to point-of-care applications. This review provides insights into the optimization of fNIRS-based tools for precision psychiatry.
Core Tip: Functional near-infrared spectroscopy shows great potential in depression research: It captures prefrontal oxygenation deficits at rest and emotional/cognitive activation abnormalities during tasks, offering quantifiable biomarkers for diagnosis, differentiation, and severity assessment. Despite challenges like limited spatial resolution and unstandardized tasks, combining with advanced algorithms and large samples may enable its key role in early screening, mechanism analysis, and precise treatment, advancing objective, quantitative diagnosis.
