Copyright
©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Healthy individuals vs patients with bipolar or unipolar depression in gray matter volume
Yin-Nan Zhang, Hui Li, Zhi-Wei Shen, Chang Xu, Yue-Jun Huang, Ren-Hua Wu
Yin-Nan Zhang, Department of Rehabilitation Medicine, Mental Health Center of Shantou University, Shantou 515000, Guangdong Province, China
Hui Li, Mental Health Center of Shantou University, Shantou 515000, Guangdong Province, China
Zhi-Wei Shen, Philips Healthcare China, Beijing 100000, China
Chang Xu, Mental Health Center of Shantou University, Shantou 515000, Guangdong Province, China
Yue-Jun Huang, Department of Pediatrics, The Second Affiliated Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
Ren-Hua Wu, Department of Medical Imaging, The Second Affiliated Hospital of Shantou University Medical College, Shantou 515041, Guangdong Province, China
Author contributions: Li H designed the study; Zhang YN diagnosed and treated the patients and participated in data collection; Xu C and Shen ZW performed imaging examination and analysis; Huang YJ followed the patients to assess their outcomes; Zhang YN and Wu RH conducted data analysis and prepared the manuscript; All authors approved the final version of the manuscript.
Supported by the Youth Fund of National Natural Science Foundation of China, No. 81701338; And the Shantou Medical Science and Technology Plan Project, No. 20150406.
Institutional review board statement: The study protocol was approved by the ethics committee of Shantou University Medical College ([2017]0301).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All authors declare that they have no conflicts of interest to report.
Data sharing statement: Data are available upon reasonable request from corresponding author.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Ren-Hua Wu, MD, Professor, Department of Medical Imaging, The Second Affiliated Hospital of Shantou University Medical College, Dongxia North Road, Shantou 515041, Guangdong Province, China.
rhwu@stu.edu.cn
Received: September 10, 2020
Peer-review started: September 10, 2020
First decision: November 30, 2020
Revised: December 14, 2020
Accepted: December 23, 2020
Article in press: December 23, 2020
Published online: February 26, 2021
Processing time: 149 Days and 7.6 Hours
ARTICLE HIGHLIGHTS
Research background
Previous studies using voxel-based morphometry (VBM) revealed changes in gray matter volume (GMV) patients with depression.
Research motivation
The differences of GMV using VBM between bipolar disorder (BD) and unipolar depression (UD) are less known.
Research objectives
To analyze the whole-brain GMV data of patients with untreated UD and BD compared with healthy controls.
Research methods
Patients with BD, those with UD, and non-depressive controls were enrolled to further analyze the brain images.
Research results
There were differences in GMV between UD patients and controls, as well as between BD patients and controls. There were no differences in GMV between UD and BD patients.
Research conclusions
BM might have a low value for differentiating between UD and BD. However, patients with UD and BD had different patterns of changes in GMV when compared with healthy controls.
Research perspectives
Classification techniques based on machine learning could be explored.
