One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity.

Cerebral cortex communications Pub Date : 2022-07-25 eCollection Date: 2022-01-01 DOI:10.1093/texcom/tgac027
Eleonora De Filippi, Theo Marins, Anira Escrichs, Matthieu Gilson, Jorge Moll, Fernanda Tovar-Moll, Gustavo Deco
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引用次数: 2

Abstract

In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain's structure and function by shedding light on the direct connections between brain areas affected by NFB training.

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一组fmri -神经反馈运动意象训练调节全脑有效连通性和动态复杂性。
在过去的十年中,一些研究表明,功能磁共振成像的神经反馈(NFB)可以改变靶区和非靶区的功能耦合。然而,这些变化背后的因果机制仍然不确定。在这里,我们应用全脑动态模型来估计17名接受运动意象NFB训练的参与者和16名接受假反馈的健康对照者在单次NFB训练前后获得的静息状态数据的有效连通性(EC)特征。组内和组间分类分析显示,只有NFB组可以准确区分两种静息状态会话。NFB训练相关的特征反映在奖赏处理和内隐学习相关区域之间的直接连接的支持网络中,以及属于躯体运动、控制、注意和默认模式网络的区域,通过递归特征消除程序识别。通过应用数据驱动的方法来探索NFB诱导的时空动态变化,我们证明,只有在真正的NFB训练后,这些区域在不同大脑状态(即亚稳态)之间的切换也会减少。总的来说,我们的研究结果通过揭示受NFB训练影响的大脑区域之间的直接联系,有助于理解NFB对整个大脑结构和功能的影响。
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