WITHDRAWN: Neurofeedback-mediated self-regulation of the dopaminergic midbrain

doi:10.1016/j.neuroimage.2013.02.041

NeuroImage

Volume 75, 15 July 2013, Page 176

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The publisher regrets due to an error in the publishing process the above articles was accidentally withdrawn and has now been published in (Neuroimage 83C December 2013 https://doi.org/10.1016/j.neuroimage.2013.05.115).

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Cited by (31)

Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis
2021, NeuroImage
Citation Excerpt :
Neurofeedback paradigms in clinical populations have oftentimes been piloted more thoroughly, and sometimes even follow a series of several neurofeedback studies in healthy populations which serve as pilots or templates for implementing the optimized final neurofeedback patient studies. For instance, Kirschner et al., (2018) trained participants with cocaine use disorder to regulate their dopaminergic midbrain using a paradigm that had been previously successfully applied to healthy participants (Sulzer et al., 2013). Consequently, high risk studies that are more likely to show a high percentage of unsuccessful neurofeedback runs, e.g. studies using a novel analysis method or an ultra-high-field MRI scanner, might be less often performed with patient populations.
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 research practices and data sharing.
Training emotion regulation through real-time fMRI neurofeedback of amygdala activity
2019, NeuroImage
Being in control of one's emotions is not only desirable in many everyday situations but is also a great challenge in a variety of mental disorders. Successful intentional emotion regulation is related to down-regulation of amygdala activity. Training mental interventions supported by neurofeedback of one's own amygdala activity using real-time (rt-)fMRI might be beneficial for mental health and well-being. Rt-fMRI guided amygdala-downregulation using cognitive interventions such as a “reality check”, however, have not been well-investigated.
Fifteen healthy subjects underwent four rt-fMRI sessions with neurofeedback of their own amygdala activity while applying a reality check as an emotion regulation strategy in order to down-regulate their amygdala signal during a stimulation with emotional pictures. The Control group comprised of eleven subjects also trained emotion regulation but without obtaining feedback. We hypothesized more prominent down-regulation of amygdala activity at the end of the training in the Feedback group. We investigated effects over time and between groups and further task specific connectivity of the amygdala by using psychophysiological interaction analyses.
Four weekly amygdala-based feedback sessions resulted in significantly decreased amygdala activity (p = 0.003, d = 0.93), also compared to the Control group (p = 0.014, d = 1.12). Task specific connectivity of the amygdala with the anterior cingulate cortex, hippocampus and distinct prefrontal areas was increased in the Feedback group.
Training of emotion regulation supported by rt-fMRI neurofeedback resulted in a prominent amygdala down-regulation compared to training without feedback. The finding implicates successful emotion regulation, compliant with emotion control models, through an easily applicable reality check strategy. Rt-fMRI neurofeedback may support emotion regulation learning and bears clinical potential for psychotherapy.
Voluntary control of anterior insula and its functional connections is feedback-independent and increases pain empathy
2016, NeuroImage
Citation Excerpt :
Importantly, initial clinical studies have also produced promising results, suggesting that rtfMRI-NF is a safe and efficient non-invasive strategy for helping to modify aberrant brain activity patterns in psychiatric populations (Hawkinson et al., 2012; Linden, 2014; Stoeckel et al., 2014), including patients with major depression (Linden et al., 2012; Young et al., 2014), contamination anxiety (Scheinost et al., 2013) and schizophrenia (Ruiz et al., 2013). Nevertheless, research has only just begun to explore the therapeutic application of rtfMRI-NF as a neuromodulatory strategy and several important issues need to be clarified (Linden, 2014; Stoeckel et al., 2014; Sulzer et al., 2013). As with other novel neuromodulatory treatment strategies, such as transcranial magnetic stimulation (TMS) (Rossi et al., 2009), the therapeutic potential of rtfMRI-NF particularly depends on: (1) whether the alterations in brain activity are of functional relevance (i.e. affect emotional or cognitive processing of the individual both in terms of behavior and alterations in functional circuitry in the brain), and (2) whether the neuromodulatory effects last beyond the duration of initial training in the absence of further feedback.
Real-time functional magnetic resonance imaging (rtfMRI)-assisted neurofeedback (NF) training allows subjects to acquire volitional control over regional brain activity. Emerging evidence suggests its potential clinical utility as an effective non-invasive treatment approach in mental disorders. The therapeutic potential of rtfMRI-NF training depends critically upon whether: (1) acquired self-regulation produces functionally relevant changes at behavioral and brain network levels and (2) training effects can be maintained in the absence of feedback. To address these key questions, the present study combined rtfMRI-NF training for acquiring volitional anterior insula (AI) regulation with a sham-controlled between-subject design. The functional relevance of acquired AI control was assessed using both behavioral (pain empathy) and neural (activity, functional connectivity) indices. Maintenance of training effects in the absence of feedback was assessed two days later. During successful acquisition of volitional AI up-regulation subjects exhibited stronger empathic responses, increased AI-prefrontal coupling in circuits involved in learning and emotion regulation and increased resting state connectivity within AI-centered empathy networks. At follow-up both self-regulation and increased connectivity in empathy networks were fully maintained, although without further increases in empathy ratings. Overall these findings support the potential clinical application of rtfMRI-NF for inducing functionally relevant and lasting changes in emotional brain circuitry.
Manipulating motor performance and memory through real-time fMRI neurofeedback
2015, Biological Psychology
Citation Excerpt :
To shed further light on the neural substrate of neurofeedback learning, we applied whole brain analyses to reveal brain activations extending beyond the SMA and PHC ROIs. Similar to previous real-time fMRI neurofeedback studies, we found that self-regulation resulted in widespread brain activations (e.g. Chiew et al., 2012; Haller et al., 2013; Rota, Handjaras, Sitaram, Birbaumer, & Dogil, 2011; Subramanian et al., 2011; Sulzer et al., 2013b; Veit et al., 2012; Zotev et al., 2011). These activations included the SMA and PHC ROIs, attention-related parietal areas, cingulate areas which might be involved in reward-based learning, and areas related to skill learning such as the putamen (Fig. 5; Table 1).
Task performance depends on ongoing brain activity which can be influenced by attention, arousal, or motivation. However, such modulating factors of cognitive efficiency are unspecific, can be difficult to control, and are not suitable to facilitate neural processing in a regionally specific manner. Here, we non-pharmacologically manipulated regionally specific brain activity using technically sophisticated real-time fMRI neurofeedback. This was accomplished by training participants to simultaneously control ongoing brain activity in circumscribed motor and memory-related brain areas, namely the supplementary motor area and the parahippocampal cortex. We found that learned voluntary control over these functionally distinct brain areas caused functionally specific behavioral effects, i.e. shortening of motor reaction times and specific interference with memory encoding. The neurofeedback approach goes beyond improving cognitive efficiency by unspecific psychological factors such as attention, arousal, or motivation. It allows for directly manipulating sustained activity of task-relevant brain regions in order to yield specific behavioral or cognitive effects.
Neurofeedback for ADHD: A Review of Current Evidence
2014, Child and Adolescent Psychiatric Clinics of North America
Citation Excerpt :
Both near infrared spectroscopy neurofeedback70 and real-time fMRI neurofeedback may offer advantages in terms of targeting well-defined brain regions, and such studies are ongoing.71 Notably, real-time fMRI additionally opens the possibility for more rapid learning to regulate deep structures, such as the dopaminergic midbrain regions72 implicated in ADHD along with cortical regions.73 With regard to the multiple identified pathways to ADHD, initial steps have been undertaken to target deficits in executive dysfunction (eg, making use of inhibition or working memory trainings; see the article by Sonuga-Barke and colleagues elsewhere in this issue) and reward-related impairments.
Self-regulation of visual word form area activation with real-time fMRI neurofeedback
2023, Scientific Reports

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¹: Co-last authors.

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