Feng Yu, Zeng Weiming, Xie Yifan, Chen Hongyu, Wang Lei, Wang Yingying, Yan Hongjie, Zhang Kaile, Tao Ran, Siok Wai Ting, Wang Nizhuan
Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang 222002, China.
Tomography. 2024 Dec 9;10(12):2014-2037. doi: 10.3390/tomography10120144.
Although it has been noticed that depressed patients show differences in processing emotions, the precise neural modulation mechanisms of positive and negative emotions remain elusive. FMRI is a cutting-edge medical imaging technology renowned for its high spatial resolution and dynamic temporal information, making it particularly suitable for the neural dynamics of depression research.
To address this gap, our study firstly leveraged fMRI to delineate activated regions associated with positive and negative emotions in healthy individuals, resulting in the creation of the positive emotion atlas (PEA) and the negative emotion atlas (NEA). Subsequently, we examined neuroimaging changes in depression patients using these atlases and evaluated their diagnostic performance based on machine learning.
Our findings demonstrate that the classification accuracy of depressed patients based on PEA and NEA exceeded 0.70, a notable improvement compared to the whole-brain atlases. Furthermore, ALFF analysis unveiled significant differences between depressed patients and healthy controls in eight functional clusters during the NEA, focusing on the left cuneus, cingulate gyrus, and superior parietal lobule. In contrast, the PEA revealed more pronounced differences across fifteen clusters, involving the right fusiform gyrus, parahippocampal gyrus, and inferior parietal lobule.
These findings emphasize the complex interplay between emotion modulation and depression, showcasing significant alterations in both PEA and NEA among depression patients. This research enhances our understanding of emotion modulation in depression, with implications for diagnosis and treatment evaluation.
尽管人们已经注意到抑郁症患者在情绪处理方面存在差异,但正负性情绪的确切神经调节机制仍不清楚。功能磁共振成像(fMRI)是一种前沿的医学成像技术,以其高空间分辨率和动态时间信息而闻名,使其特别适合用于抑郁症的神经动力学研究。
为了填补这一空白,我们的研究首先利用功能磁共振成像来描绘健康个体中与正负性情绪相关的激活区域,从而创建了正性情绪图谱(PEA)和负性情绪图谱(NEA)。随后,我们使用这些图谱检查了抑郁症患者的神经影像学变化,并基于机器学习评估了它们的诊断性能。
我们的研究结果表明,基于PEA和NEA对抑郁症患者的分类准确率超过了0.70,与全脑图谱相比有显著提高。此外,低频振幅(ALFF)分析揭示了在NEA期间,抑郁症患者与健康对照在八个功能簇上存在显著差异,主要集中在左侧楔叶、扣带回和顶上小叶。相比之下,PEA显示在15个簇上有更明显的差异,涉及右侧梭状回、海马旁回和顶下小叶。
这些发现强调了情绪调节与抑郁症之间复杂的相互作用,显示出抑郁症患者的PEA和NEA均有显著改变。这项研究加深了我们对抑郁症中情绪调节的理解,对诊断和治疗评估具有重要意义。