Li Wenjie, Zhang Wei, Jiang Zhongyi, Zhou Tiantong, Xu Shoukun, Zou Ling
School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China.
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China.
Front Hum Neurosci. 2022 Aug 12;16:960784. doi: 10.3389/fnhum.2022.960784. eCollection 2022.
The neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.
We proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.
EEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive reappraisal task. Fused priori weighted minimum norm estimation (FWMNE) with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index (PLI) was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.
The functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.
The proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders.
基于情绪的认知重评的神经活动和功能网络已通过脑电图(EEG)和功能磁共振成像(fMRI)得到广泛研究。然而,单模态神经成像技术在探索具有高时间和空间分辨率的调节过程方面存在局限性。
我们提出了一种EEG和fMRI多模态整合的源定位方法,并在情绪认知重评的源水平功能网络分析中对其进行了测试。
在15名受试者执行情绪认知重评任务时,同时记录EEG和fMRI数据。提出了带有滑动窗口的融合先验加权最小范数估计(FWMNE)来追踪EEG源活动的动态变化,并使用相位滞后指数(PLI)构建与使用重评策略下调负面影响过程相关的功能性脑网络。
使用PLI测量构建了功能网络,并指出了其中的重要区域。在γ波段源水平网络分析中,楔叶、外侧眶额皮质、顶上叶皮质、中央后回和岛盖部被确定为具有高介数中心性的重评重要区域。
所提出的多模态整合源定位方法确定了参与情绪调节的关键皮质,网络分析展示了参与重评认知控制的重要脑区。它在情感障碍临床应用中显示出前景。