Lab of Neural Engineering & Rehabilitation, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
J Affect Disord. 2022 Nov 15;317:278-286. doi: 10.1016/j.jad.2022.08.128. Epub 2022 Aug 31.
Subclinical depression (SD) and major depressive disorder (MDD) can be considered as the early and late stages of depression, but the characteristics of intrinsic neural activity in different depressive stages are largely unknown.
Twenty-six SD, 36 MDD subjects and 33 well-matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Voxel-wise regional homogeneity (ReHo) was analyzed to explore the alterations of intrinsic neural activity, and machine learning classification based on ReHo features was performed to assess potential performance for diagnostic classification.
Common alterations of ReHo in both SD and MDD groups were found in the bilateral middle temporal gyrus and the left middle occipital gyrus. Opposite alterations in SD and MDD groups were found in the right superior cerebellum. Moreover, increased ReHo in the bilateral precuneus was only found in MDD, while increased ReHo in the right middle frontal gyrus and precentral gyrus were unique to SD. The distinct ReHo values correctly identified SD, MDD, and HC by linear support vector machine (SVM) with an accuracy of 77.89 %, which further verified the discrimination ability of altered ReHo in these brain regions.
The sample size is relatively small.
Common and unique ReHo alterations provided insights into the development of brain impairments in depression, and helped to understand the pathophysiology of SD and MDD.
亚临床抑郁(SD)和重度抑郁障碍(MDD)可被视为抑郁的早期和晚期阶段,但不同抑郁阶段内在神经活动的特征在很大程度上尚不清楚。
招募了 26 名 SD、36 名 MDD 受试者和 33 名匹配良好的健康对照者(HCs),并进行了静息态功能磁共振成像(rs-fMRI)。采用体素水平局部一致性(ReHo)分析来探讨内在神经活动的变化,并基于 ReHo 特征进行机器学习分类,以评估潜在的诊断分类性能。
在 SD 和 MDD 组中均发现双侧颞中回和左侧中枕叶回的 ReHo 常见改变。SD 和 MDD 组的右侧小脑上叶呈现相反的改变。此外,MDD 组双侧楔前叶的 ReHo 增加,而 SD 组右侧额中回和中央前回的 ReHo 增加是其独特表现。线性支持向量机(SVM)通过明显的 ReHo 值正确识别了 SD、MDD 和 HC,准确率为 77.89%,进一步验证了这些脑区改变的 ReHo 值的区分能力。
样本量相对较小。
共同和独特的 ReHo 改变为理解抑郁中大脑损伤的发展提供了新的视角,并有助于理解 SD 和 MDD 的病理生理学。