Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback
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. Maladaptation of this dynamic resource reallocation process is thought to play a m...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921008004 |
| _version_ | 1818935019706515456 |
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| author | Florian Krause Nikos Kogias Martin Krentz Michael Lührs Rainer Goebel Erno J. Hermans |
| author_facet | Florian Krause Nikos Kogias Martin Krentz Michael Lührs Rainer Goebel Erno J. Hermans |
| author_sort | Florian Krause |
| collection | DOAJ |
| description | 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. Maladaptation 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. |
| first_indexed | 2024-12-20T05:13:31Z |
| format | Article |
| id | doaj.art-3cf684f0343b4ea1a4a0c1333e48ece8 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-20T05:13:31Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-3cf684f0343b4ea1a4a0c1333e48ece82022-12-21T19:52:14ZengElsevierNeuroImage1095-95722021-11-01243118527Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedbackFlorian Krause0Nikos Kogias1Martin Krentz2Michael Lührs3Rainer Goebel4Erno J. Hermans5Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands; Corresponding author.Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the NetherlandsDonders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the NetherlandsDepartment of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Research and Development, Brain Innovation B.V., Maastricht, the NetherlandsDepartment of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Research and Development, Brain Innovation B.V., Maastricht, the NetherlandsDonders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the NetherlandsIt 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. Maladaptation 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.http://www.sciencedirect.com/science/article/pii/S1053811921008004Real-time fMRINeurofeedbackLarge-scale brain networksStressResilience |
| spellingShingle | Florian Krause Nikos Kogias Martin Krentz Michael Lührs Rainer Goebel Erno J. Hermans Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback NeuroImage Real-time fMRI Neurofeedback Large-scale brain networks Stress Resilience |
| title | Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback |
| title_full | Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback |
| title_fullStr | Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback |
| title_full_unstemmed | Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback |
| title_short | Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback |
| title_sort | self regulation of stress related large scale brain network balance using real time fmri neurofeedback |
| topic | Real-time fMRI Neurofeedback Large-scale brain networks Stress Resilience |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921008004 |
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