Pamplona Gustavo S P, Zweerings Jana, Lor Cindy S, deErney Lindsay, Roecher Erik, Taebi Arezoo, Hellrung Lydia, Amano Kaoru, Scheinost Dustin, Krause Florian, Rosenberg Monica D, Ionta Silvio, Brem Silvia, Hermans Erno J, Mathiak Klaus, Scharnowski Frank
Department of Ophthalmology/University of Lausanne, SensoriMotorLab, Jules-Gonin Eye Hospital/Fondation Asile Des Aveugles, Lausanne, Switzerland.
Department of Health Sciences and Technology, Rehabilitation Engineering Laboratory (RELab), ETH Zurich, Zurich, Switzerland.
Hum Brain Mapp. 2025 Jul;46(10):e70279. doi: 10.1002/hbm.70279.
The acquisition of new skills is facilitated by providing individuals with feedback that reflects their performance. This process creates a closed loop that involves feedback processing and regulation recalibration to promote effective training. Functional magnetic resonance imaging (fMRI)-based neurofeedback is unique in applying this principle by delivering direct feedback on the self-regulation of brain activity. Understanding how feedback-driven learning occurs requires examining how feedback is evaluated and how regulation adjusts in response to feedback signals. In this pre-registered mega-analysis, we re-analyzed data from eight intermittent fMRI neurofeedback studies (N = 153 individuals) to investigate brain regions where activity and connectivity are linked to feedback processing and regulation recalibration (i.e., regulation after feedback) during training. We harmonized feedback scores presented during training in these studies and computed their linear associations with brain activity and connectivity using parametric general linear model analyses. We observed that, during feedback processing, feedback scores were positively associated with (1) activity in the reward system, dorsal attention network, default mode network, and cerebellum; and with (2) reward system-related connectivity within the salience network. During regulation recalibration, no significant associations were observed between feedback scores and either activity or associative learning-related connectivity. Our results suggest that neurofeedback is processed in the reward system, supporting the theory that reinforcement learning shapes this form of brain training. In addition, the involvement of large-scale networks in feedback processing, continuously transitioning between evaluating external feedback and internally assessing the adopted cognitive state, suggests that higher-level processing is integral to neurofeedback learning, which usually occurs over a short time span. Our findings highlight the pivotal role of performance-related feedback as a driving force in learning, potentially extending beyond neurofeedback training to other feedback-based processes.
通过向个体提供反映其表现的反馈，有助于新技能的习得。这一过程形成了一个闭环，其中涉及反馈处理和调节重新校准，以促进有效的训练。基于功能磁共振成像（fMRI）的神经反馈在应用这一原理方面独具特色，它能对大脑活动的自我调节提供直接反馈。要理解反馈驱动的学习是如何发生的，需要研究反馈是如何被评估的，以及调节是如何根据反馈信号进行调整的。在这项预先注册的大型分析中，我们重新分析了八项间歇性fMRI神经反馈研究（N = 153名个体）的数据，以调查在训练过程中，哪些脑区的活动和连接与反馈处理和调节重新校准（即反馈后的调节）相关。我们对这些研究中训练期间呈现的反馈分数进行了统一，并使用参数化一般线性模型分析计算了它们与大脑活动和连接的线性关联。我们观察到，在反馈处理过程中，反馈分数与以下方面呈正相关：（1）奖励系统、背侧注意网络、默认模式网络和小脑的活动；以及（2）突显网络内与奖励系统相关的连接。在调节重新校准过程中，未观察到反馈分数与活动或关联学习相关连接之间存在显著关联。我们的结果表明，神经反馈在奖励系统中进行处理，支持了强化学习塑造这种大脑训练形式的理论。此外，大规模网络参与反馈处理，在评估外部反馈和内部评估所采用的认知状态之间不断转换，这表明高级处理对于神经反馈学习至关重要，而神经反馈学习通常在短时间内发生。我们的研究结果突出了与表现相关的反馈作为学习驱动力的关键作用，这一作用可能不仅限于神经反馈训练，还可能扩展到其他基于反馈的过程。
