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

Krause, Florian; Kogias, Nikos; Krentz, Martin; Lührs, Michael; Goebel, Rainer; Hermans, Erno J.

doi:10.1016/j.neuroimage.2021.118527

Cited by 15 publications

(15 citation statements)

References 57 publications

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“…Recent fMRI studies have suggested aINS to be part of the "salience network", important in attentional control and detecting behaviorally salient stimuli [84][85][86]. Various factors may contribute to activation in the salience network, including increases in cognitive effort, reward anticipation [87,88], heightened sympathetic arousal [89], and the pressure to per-form self-regulation within limited time period [90]. Moreover, aINS is critical in detecting discrepancies between actual and desired states [91,92], as well as integrating the feedback display (external) with brain activity (internal) [93].…”

Section: Discussionmentioning

confidence: 99%

Neurofeedback-Augmented Mindfulness Training Elicits Distinct Responses in the Subregions of the Insular Cortex in Healthy Adolescents

Cohen

Tsuchiyagaito

et al. 2022

Brain Sciences

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Mindfulness training (MT) reduces self-referential processing and promotes interoception, the perception of sensations from inside the body, by increasing one’s awareness of and regulating responses to them. The posterior cingulate cortex (PCC) and the insular cortex (INS) are considered hubs for self-referential processing and interoception, respectively. Although MT has been consistently found to decrease PCC, little is known about how MT relates to INS activity. Understanding links between mindfulness and interoception may be particularly important for informing mental health in adolescence, when neuroplasticity and emergence of psychopathology are heightened. We examined INS activity during real-time functional magnetic resonance imaging neurofeedback-augmented mindfulness training (NAMT) targeting the PCC. Healthy adolescents (N = 37; 16 female) completed the NAMT task, including Focus-on-Breath (MT), Describe (self-referential processing), and Rest conditions, across three neurofeedback runs and two non-neurofeedback runs (Observe, Transfer). Regression coefficients estimated from the generalized linear model were extracted from three INS subregions: anterior (aINS), mid (mINS), and posterior (pINS). Mixed model analyses revealed the main effect of run for Focus-on-Breath vs. Describe contrast in aINS [R2 = 0.39] and pINS [R2 = 0.33], but not mINS [R2 = 0.34]. Post hoc analyses revealed greater aINS activity and reduced pINS activity during neurofeedback runs, and such activities were related to lower self-reported life satisfaction and less pain behavior, respectively. These findings revealed the specific involvement of insula subregions in rtfMRI-nf MT.

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Section: Discussionmentioning

confidence: 99%

Neurofeedback-Augmented Mindfulness Training Elicits Distinct Responses in the Subregions of the Insular Cortex in Healthy Adolescents

Cohen

Tsuchiyagaito

et al. 2022

Brain Sciences

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“…As mentioned beforehand a reporting of these transfer and processing times would help to get more insights is the spread of the problem. In a variety of publications (Hampson et al, 2011; Hellrung et al, 2018; Krause et al, 2021; Marxen et al, 2016) a similar method for the direct data transfer are mentioned allowing to argue in both ways that the potential consequences are not as strong since solutions were already in place.…”

Section: Resultsmentioning

confidence: 99%

Retrieving fMRI data in real-time: difficulties and pitfalls

Lührs

Poser

Auer

et al. 2022

Preprint

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One of the significant challenges in real-time fMRI environments is to ensure that the functional images are exported in real-time. The prerequired ability to reconstruct these images immediately after the acquisition has already been resolved in 2004. Nowadays, more sophisticated sequences allow for higher resolution and faster repetition times and thereby challenging the ability to export this data in real-time. In this article, we tackle the potentially arising problem of sending the reconstructed data from the MRI to an external PC to perform the real-time fMRI analysis. We show that depending on the implementation of the data transfer, long delays can occur that can differ drastically in time and how often they occur. In addition, we propose a solution for SIEMENS MRI devices which was tested and applied already on multiple MRI devices including 3T and 7T machines on different vendor software versions. This new technique can be used as a blueprint that can be directly applied to other manufacturers. We also provide the source code of the described solution and show that the delay in the data transfer can be completely resolved using our proposed procedure. Finally, we integrate measurement options for the data transfer times to improve quality measures in real-time fMRI environments (e.g., clinical) that can implement the proposed solution. Efforts should be taken by the real-time community and MRI manufacturers to employ a standardized real-time export e.g., similar to the lab streaming layer which is used as a standard export method in EEG environments.

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“…Network Neurofeedback (NetNef) based on large-scale brain network configurations now allows for directly targeting the wide-spread neural changes underlying stress and stress-related psychopathology, by modulating entire functionally related networks that each consist of multiple brain areas. Several proof-of-concept studies already pioneered this new type of Neurofeedback by successfully demonstrating that it is generally feasible to train individuals to self-regulate complex neural parameters that are based on (1) network connectivity between SN and ECN (Kim et al, 2019), (2) network balance between the activations of SN and ECN (Krause et al, 2021), ECN and DMN (Bauer et al, 2020), or DMN and the sustained attention network (Pamplona et al, 2020), as well as (3) full-brain connectomes (Heunis et al, 2018;Scheinost et al, 2020). These new paradigms provide very promising solutions for addressing core deficits of stress-related health issues, with the potential for future clinical applications to decrease auditory hallucinations (Bauer et al, 2020), increase sustained attention (Pamplona et al, 2020;Scheinost et al, 2020), support mindfulness (Kim et al, 2019), and strengthen overall resilience to stressors (Krause et al, 2021).…”

Section: A Network Solution For Network Disordersmentioning

confidence: 99%

“…Several proof-of-concept studies already pioneered this new type of Neurofeedback by successfully demonstrating that it is generally feasible to train individuals to self-regulate complex neural parameters that are based on (1) network connectivity between SN and ECN (Kim et al, 2019), (2) network balance between the activations of SN and ECN (Krause et al, 2021), ECN and DMN (Bauer et al, 2020), or DMN and the sustained attention network (Pamplona et al, 2020), as well as (3) full-brain connectomes (Heunis et al, 2018;Scheinost et al, 2020). These new paradigms provide very promising solutions for addressing core deficits of stress-related health issues, with the potential for future clinical applications to decrease auditory hallucinations (Bauer et al, 2020), increase sustained attention (Pamplona et al, 2020;Scheinost et al, 2020), support mindfulness (Kim et al, 2019), and strengthen overall resilience to stressors (Krause et al, 2021). Given the very active field of network neuroscience (Lydon-Staley & Bassett, 2018) and ongoing approaches to combine this new knowledge with network theories in psychopathology (Blanken et al, 2021), the interest in NetNef can be expected to increase, with fast progress and many more clinical applications in the future.…”

Section: A Network Solution For Network Disordersmentioning

confidence: 99%

Getting stress-related disorders under control: The unique opportunity of neurofeedback-based therapy

Krause¹,

Linden²,

Hermans³

2022

Preprint

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Stress-related mental health issues are not only highly prevalent world-wide, but are also associated with a very high disease burden. Moreover, efforts to reduce their prevalence through standard therapy have remained largely unsuccessful, making stress-related disorders a fast-progressing global societal problem that needs urgent attention and new solutions to mitigate it. One potential solution might come from recent advances in neuroscience which have opened the door for new innovative approaches that embrace new insights about the underlying neural working mechanisms of stress and combine them with non-invasive neuro-technology. One particularly promising approach in this field is that of neurofeedback. In this article, we argue that neurofeedback has a unique set of properties which can give a neurofeedback-based therapy approach an advantage over other (standard) treatment approaches, and might make it especially suited for mitigating stress-related mental health issues.

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

Cited by 15 publications

References 57 publications

Neurofeedback-Augmented Mindfulness Training Elicits Distinct Responses in the Subregions of the Insular Cortex in Healthy Adolescents

Neurofeedback-Augmented Mindfulness Training Elicits Distinct Responses in the Subregions of the Insular Cortex in Healthy Adolescents

Retrieving fMRI data in real-time: difficulties and pitfalls

Getting stress-related disorders under control: The unique opportunity of neurofeedback-based therapy

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