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三重网络的中介分析揭示了正念从实时 fMRI 神经反馈中的功能特征。

Mediation analysis of triple networks revealed functional feature of mindfulness from real-time fMRI neurofeedback.

机构信息

Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.

Division of Clinical Psychology and Psychiatry, Department of Psychology, University of Basel, Basel, Switzerland.

出版信息

Neuroimage. 2019 Jul 15;195:409-432. doi: 10.1016/j.neuroimage.2019.03.066. Epub 2019 Apr 3.

Abstract

The triple networks, namely the default-mode network (DMN), the central executive network (CEN), and the salience network (SN), play crucial roles in disorders of the brain, as well as in basic neuroscientific processes such as mindfulness. However, currently, there is no consensus on the underlying functional features of the triple networks associated with mindfulness. In this study, we tested the hypothesis that (a) the partial regression coefficient (i.e., slope): from the SN to the DMN, mediated by the CEN, would be one of the potential mindfulness features in the real-time functional magnetic resonance imaging (rtfMRI) neurofeedback (NF) setting, and (b) this slope level may be enhanced by rtfMRI-NF training. Sixty healthy mindfulness-naïve males participated in an MRI session consisting of two non-rtfMRI-runs, followed by two rtfMRI-NF runs and one transfer run. Once the regions-of-interest of each of the triple networks were defined using the non-rtfMRI-runs, the slope level was calculated by mediation analysis and used as neurofeedback information, in the form of a thermometer bar, to assist with participant mindfulness during the rtfMRI-NF runs. The participants were asked to increase the level of the thermometer bar while deploying a mindfulness strategy, which consisted of focusing attention on the physical sensations of breathing. rtfMRI-NF training was conducted as part of a randomized controlled trial design, in which participants were randomly assigned to either an experimental group or a control group. The participants in the experimental group received contingent neurofeedback information, which was obtained from their own brain signals, whereas the participants in the control group received non-contingent neurofeedback information that originated from matched participants in the experimental group. Our results indicated that the slope level from the SN to the DMN, mediated by the CEN, was associated with mindfulness score (rtfMRI-NF runs: r = 0.53, p = 0.007; p-value was corrected from 10,000 random permutations) and with task-performance feedback score (rtfMRI-NF run: r = 0.61, p = 0.001) in the experimental group only. In addition, during the rtfMRI-NF runs the level of the partial regression coefficient feature was substantially increased in the experimental group compared to the control group (p < 0.05 from the paired t-test; the p-value was corrected from 10,000 random permutations). To the best of our knowledge, this is the first study to demonstrate a partial regression coefficient feature of mindfulness in the rtfMRI-NF setting obtained by triple network mediation analysis, as well as the possibility of enhancement of the partial regression coefficient feature by rtfMRI-NF training.

摘要

三重网络,即默认模式网络(DMN)、中央执行网络(CEN)和突显网络(SN),在大脑疾病以及正念等基础神经科学过程中起着至关重要的作用。然而,目前对于与正念相关的三重网络的潜在功能特征还没有共识。在这项研究中,我们检验了以下假设:(a) 从中枢执行网络(CEN)介导的突显网络(SN)到默认模式网络(DMN)的部分回归系数(即斜率),在实时功能磁共振成像(rtfMRI)神经反馈(NF)设置中,是正念的一个潜在特征;(b) 这种斜率水平可能会通过 rtfMRI-NF 训练得到增强。60 名健康的正念初学者男性参加了一项 MRI 实验,该实验包括两个非 rtfMRI 运行,随后是两个 rtfMRI-NF 运行和一个转移运行。一旦使用非 rtfMRI 运行确定了三重网络的每个区域的兴趣区域,就通过中介分析计算斜率水平,并将其用作神经反馈信息,以温度计条的形式,在 rtfMRI-NF 运行期间协助参与者保持正念。参与者被要求在部署正念策略时增加温度计条的水平,该策略包括将注意力集中在呼吸的身体感觉上。rtfMRI-NF 训练是作为一项随机对照试验设计的一部分进行的,其中参与者被随机分配到实验组或对照组。实验组接受的是由自身脑信号获得的有条件神经反馈信息,而对照组则接受的是来自实验组匹配参与者的无条件神经反馈信息。我们的结果表明,从中枢执行网络(CEN)介导的突显网络(SN)到默认模式网络(DMN)的斜率水平与正念评分(rtfMRI-NF 运行:r=0.53,p=0.007;p 值从 10,000 次随机排列中校正)和任务绩效反馈评分(rtfMRI-NF 运行:r=0.61,p=0.001)相关,仅在实验组中。此外,在 rtfMRI-NF 运行期间,实验组的部分回归系数特征水平与对照组相比显著增加(配对 t 检验的 p 值<0.05;从 10,000 次随机排列中校正的 p 值)。据我们所知,这是首次通过三重网络中介分析,在 rtfMRI-NF 环境中,对正念的部分回归系数特征进行研究,并证明 rtfMRI-NF 训练可以增强部分回归系数特征。

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