Modulatory effects of dynamic fMRI-based neurofeedback on emotion regulation networks in adolescent females

Zich, Catharina; Johnstone, Nicola; Lührs, Michael; Lisk, Stephen; Haller, Simone P.; Lipp, Annalisa; Lau, Jennifer Y. F.; Kadosh, Kathrin Cohen

doi:10.1101/347971

Cited by 8 publications

(10 citation statements)

References 69 publications

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“…In a second study, we then used functional connectivity-based neurofeedback to directly modulate emotion regulation network connectivity in females aged 14 to 17 years. 48 Specifically, we were able to successfully train participants to modulate the functional coupling of the PFC and the amygdala towards a more negative connectivity pattern, which resembles the connectivity pattern found in the mature brain 48 and away from the positive connectivity patterns that predominates in younger children and anxious adults. 21 We also found several brain-behaviour correlations.…”

Section: Using Neurofeedback To Pinpoint Plasticity In the Developingmentioning

confidence: 91%

Training the anxious brain: using fMRI‐based neurofeedback to change brain activity in adolescence

Lipp

Kadosh

2020

Develop Med Child Neuro

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Anxiety disorders are a leading cause of morbidity and entail a lot of costs. Adolescence is characterized by social fears and poor emotion regulation abilities which together increase the likelihood of the emergence of anxiety disorders. This emotion dysregulation is potentially caused by the emotion regulating brain areas, such as the prefrontal cortex and temporal cortex, that are still undergoing developmental changes throughout late adolescence. Recently, new approaches have used functional magnetic resonance imaging-based neurofeedback to help participants gain control over emotion regulation brain networks by receiving real-time feedback on their brain activity and to use effective emotion regulation abilities. In this review, we provide an overview of the developmental changes in the brain and the corresponding behavioural changes, and explore how these can be influenced during adolescence using neurofeedback. We conclude that recent studies show promising results that children and adolescents can self-regulate emotion regulation brain networks thereby supporting the development of effective emotion regulation abilities.

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Section: Using Neurofeedback To Pinpoint Plasticity In the Developingmentioning

confidence: 91%

Training the anxious brain: using fMRI‐based neurofeedback to change brain activity in adolescence

Lipp

Kadosh

2020

Develop Med Child Neuro

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“…We received data contributions from authors of 28 independent studies (Auer, Schweizer, & Frahm, 2015;Emmert, Kopel, et al, 2017;Hellrung et al, 2018;Kim et al, 2015;Kirschner et al, 2018;Kohl et al, 2019;MacInnes, Dickerson, Chen, & Adcock, 2016;Marins et al, 2015;Marxen et al, 2016;McDonald et al, 2017;Megumi, Yamashita, Kawato, & Imamizu, 2015;Nicholson et al, 2017;Pamplona et al, 2020;Papoutsi et al, 2020Papoutsi et al, , 2018Scharnowski et al, 2012Scharnowski et al, , 2015Sorger, Kamp, Weiskopf, Peters, & Goebel, 2018;Spetter et al, 2017;Yao et al, 2016;Young et al, 2017;Zich et al, 2020), covering a wide range of trained brain regions, different study designs, and participant populations.…”

Section: Included Studiesmentioning

confidence: 99%

“…In healthy participants, real-time fMRI neurofeedback training has been specifically linked to improvements in attention (e.g. DeBettencourt, Cohen, Lee, Norman, & Turk-Browne, 2015;Pamplona et al, 2020), emotion regulation (Koush et al, 2015;Paret & Hendler, 2020;Zich et al, 2020), memory (e.g. Scharnowski et al, 2015;Sherwood, Kane, Weisend, & Parker, 2016;Zhang, Yao, Zhang, Long, & Zhao, 2013), motivation (e.g.…”

Section: Introductionmentioning

confidence: 99%

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement – a Machine Learning Mega-Analysis

Haugg

Renz

Nicholson

et al. 2020

Preprint

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Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

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“…Feasibility has also been recently demonstrated in targeting anxiety in adolescecnts [144]. h) Appropriate reporting of methods and results…”

Section: D) Online and Offline Quality Control Of Signalsmentioning

confidence: 99%

Neurofeedback training in major depressive disorder: a systematic review of clinical efficacy, study quality and reporting practices

Trambaiolli¹,

Linden²,

Mehler³

2020

Preprint

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Major depressive disorder (MDD) is the leading cause of disability worldwide. Neurofeedback training has been suggested as a potential additional treatment option for MDD patients not reaching remission from psychopharmacology and psychotherapy. Here we systematically reviewed neurofeedback studies employing electroencephalography, or functional magnetic resonance-based protocols in depressive patients. From a total of 585 screened studies, 24 were included in our final sample (N=480 patients in experimental and N=194 in the control groups completing the primary endpoint). We evaluated the clinical efficacy across studies and explored its relationship with the number of sessions as a potential proxy for a dose-effect response. We also attempted to group studies according to the control condition categories currently used in the field that affect clinical outcomes in group comparisons. In most studies, MDD patients showed symptom improvement superior to the control group(s). However, most articles did not comply with the most stringent study quality and reporting practices. We conclude with recommendations on best practices for experimental designs and reporting standards for neurofeedback training.

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Modulatory effects of dynamic fMRI-based neurofeedback on emotion regulation networks in adolescent females

Cited by 8 publications

References 69 publications

Training the anxious brain: using fMRI‐based neurofeedback to change brain activity in adolescence

Training the anxious brain: using fMRI‐based neurofeedback to change brain activity in adolescence

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement – a Machine Learning Mega-Analysis

Neurofeedback training in major depressive disorder: a systematic review of clinical efficacy, study quality and reporting practices

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