Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback

被引:8
|
作者
Krause, Florian [1 ]
Kogias, Nikos [1 ]
Krentz, Martin [1 ]
Luhrs, Michael [2 ,3 ]
Goebel, Rainer [2 ,3 ]
Hermans, Erno J. [1 ]
机构
[1] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Med Ctr, Nijmegen, Netherlands
[2] Maastricht Univ, Dept Cognit Neurosci, Maastricht, Netherlands
[3] Brain Innovat BV, Dept Res & Dev, Maastricht, Netherlands
基金
欧洲研究理事会;
关键词
Real-time fMRI; Neurofeedback; Large-scale brain networks; Stress; Resilience; CONNECTIVITY; ROBUST; FLUCTUATIONS; REGISTRATION; ADAPTATION; ACTIVATION; ACCURATE;
D O I
10.1016/j.neuroimage.2021.118527
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Mal-adaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.
引用
收藏
页数:14
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