De Filippi Eleonora, Marins Theo, Escrichs Anira, Gilson Matthieu, Moll Jorge, Tovar-Moll Fernanda, Deco Gustavo
Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Carrer de Ramon Trias Fargas, 25-27, 08005 Barcelona, Catalonia, Spain.
D'Or Institute for Research and Education (IDOR), Rua Diniz Cordeiro 30, Botafogo-Rio de Janeiro, 22281-100, Brazil.
Cereb Cortex Commun. 2022 Jul 25;3(3):tgac027. doi: 10.1093/texcom/tgac027. eCollection 2022.
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.
在过去十年中,多项研究表明,通过功能磁共振成像进行的神经反馈(NFB)可以改变目标区域和非目标区域的功能耦合。然而,这些变化背后的因果机制仍不明确。在此,我们应用全脑动力学模型来估计17名接受运动想象NFB训练的参与者以及16名接受假反馈的健康对照者在单次NFB训练前和训练后立即采集的静息态数据的有效连接(EC)图谱。组内和组间分类分析表明,只有NFB组能够准确区分两个静息态阶段。与NFB训练相关的特征反映在奖励处理和内隐学习相关区域之间的直接连接支持网络中,以及通过递归特征消除程序确定的属于躯体运动、控制、注意力和默认模式网络的区域。通过应用数据驱动方法探索NFB引起的时空动力学变化,我们证明这些区域也仅在真实NFB训练后表现出不同脑状态之间切换的减少(即亚稳定性)。总体而言,我们的研究结果通过揭示受NFB训练影响的脑区之间的直接连接,有助于理解NFB对全脑结构和功能的影响。